import statsmodels
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.graphics.tsaplots import plot_acf
from datetime import datetime
import matplotlib as mpl
from dateutil.parser import parse
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as pltly
from plotly.offline import iplot
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import plotly
import pathlib
import itertools
pd.options.display.max_rows = None
pd.set_option('display.max_columns', 500)
dfpd = pd.read_csv("data/paperMachine/processminer-rare-event-mts - tag-map.csv")
dfp = pd.read_csv("data/paperMachine/processminer-rare-event-mts - data.csv")
dfp.rename(columns={'y': 'label'}, inplace=True)
#test git after test ignore
dfp.head()
| time | label | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5/1/99 0:00 | 0 | 0.376665 | -4.596435 | -4.095756 | 13.497687 | -0.118830 | -20.669883 | 0.000732 | -0.061114 | -0.059966 | -0.038189 | 0.877951 | -0.052959 | -13.306135 | 0.101068 | 0.041800 | 0.199901 | -2.327329 | -0.944167 | 3.075199 | 0.123154 | -0.104334 | -0.570710 | -9.784456 | 0.355960 | 15.842819 | -0.451973 | -0.105282 | 96 | -134.27786 | 0.058726 | -0.021645 | 9.366755 | 0.002151 | -69.187583 | 4.232571 | -0.225267 | -0.196872 | -0.072449 | -0.103732 | -0.720746 | -5.412436 | 76.679042 | -0.632727 | 1351.63286 | -0.657095 | -0.434947 | -108.77597 | 0.084856 | 10.210182 | 11.295155 | 29.984624 | 10.091721 | 0.053279 | -4.936434 | -24.590146 | 18.515436 | 3.473400 | 0.033444 | 0.953219 | 0.006076 | 0 |
| 1 | 5/1/99 0:02 | 0 | 0.475720 | -4.542502 | -4.018359 | 16.230659 | -0.128733 | -18.758079 | 0.000732 | -0.061114 | -0.059966 | -0.038189 | 0.873273 | -0.014244 | -13.306135 | 0.101108 | 0.041447 | 0.304313 | -2.340627 | -0.939994 | 3.075199 | 0.123154 | -0.104334 | -0.574861 | -9.784456 | 0.360160 | 16.491684 | -0.450451 | -0.092430 | 96 | -134.48019 | 0.058759 | -0.004579 | 9.350215 | 0.002149 | -68.585197 | 4.311490 | -0.225267 | -0.196872 | -0.059103 | -0.083895 | -0.720746 | -8.343222 | 78.181598 | -0.632727 | 1370.37895 | -0.875629 | -1.125819 | -108.84897 | 0.085146 | 12.534340 | 11.290761 | 29.984624 | 10.095871 | 0.062801 | -4.937179 | -32.413266 | 22.760065 | 2.682933 | 0.033536 | 1.090502 | 0.006083 | 0 |
| 2 | 5/1/99 0:04 | 0 | 0.363848 | -4.681394 | -4.353147 | 14.127998 | -0.138636 | -17.836632 | 0.010803 | -0.061114 | -0.030057 | -0.018352 | 1.004910 | 0.065150 | -9.619596 | 0.101148 | 0.041095 | 0.252839 | -2.353925 | -0.935824 | 3.075199 | 0.123154 | -0.104334 | -0.579013 | -9.784456 | 0.364356 | 15.972885 | -0.448927 | -0.097144 | 96 | -133.94659 | 0.058791 | -0.084658 | 9.037409 | 0.002148 | -67.838187 | 4.809914 | -0.225267 | -0.186801 | -0.048696 | -0.073823 | -0.720746 | -1.085166 | 79.684154 | -0.632727 | 1368.12309 | -0.037775 | -0.519541 | -109.08658 | 0.085436 | 18.582893 | 11.286366 | 29.984624 | 10.100265 | 0.072322 | -4.937924 | -34.183774 | 27.004663 | 3.537487 | 0.033629 | 1.840540 | 0.006090 | 0 |
| 3 | 5/1/99 0:06 | 0 | 0.301590 | -4.758934 | -4.023612 | 13.161567 | -0.148142 | -18.517601 | 0.002075 | -0.061114 | -0.019986 | -0.008280 | 0.930037 | -0.067199 | -15.196531 | 0.101188 | 0.040742 | 0.072873 | -2.367223 | -0.931651 | 3.075199 | 0.123154 | -0.104334 | -0.583165 | -9.784456 | 0.368556 | 15.608688 | -0.447404 | -0.160073 | 96 | -134.00259 | 0.058824 | -0.055118 | 9.020625 | 0.002146 | -67.091148 | 5.308343 | -0.225267 | -0.186801 | -0.047017 | -0.063752 | -0.720746 | 6.172891 | 81.186702 | -0.632727 | 1365.69145 | -0.987410 | 0.674524 | -109.56277 | 0.085726 | 17.719032 | 11.281972 | 29.984624 | 10.104660 | 0.081600 | -4.938669 | -35.954281 | 21.672449 | 3.986095 | 0.033721 | 2.554880 | 0.006097 | 0 |
| 4 | 5/1/99 0:08 | 0 | 0.265578 | -4.749928 | -4.333150 | 15.267340 | -0.155314 | -17.505913 | 0.000732 | -0.061114 | -0.030057 | -0.008280 | 0.828410 | -0.018472 | -14.609266 | 0.101229 | 0.040390 | 0.171033 | -2.380521 | -0.927478 | 3.075199 | 0.123154 | -0.104334 | -0.587316 | -9.784456 | 0.372756 | 15.606125 | -0.445879 | -0.131630 | 96 | -133.14571 | 0.058856 | -0.153851 | 9.344233 | 0.002145 | -65.991813 | 5.806771 | -0.225267 | -0.186801 | -0.057088 | -0.063752 | -0.720746 | -3.379599 | 82.689258 | -0.632727 | 1363.25786 | -0.238445 | -0.063044 | -110.03891 | 0.086016 | 16.855202 | 11.277577 | 29.984624 | 10.109054 | 0.091121 | -4.939414 | -37.724789 | 21.907251 | 3.601573 | 0.033777 | 1.410494 | 0.006105 | 0 |
dfp.columns
Index(['time', 'label', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9',
'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19',
'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29',
'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39',
'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49',
'x50', 'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59',
'x60', 'x61'],
dtype='object')
dfp["time"]= pd.to_datetime(dfp["time"])
dfp['time']=dfp['time'].dt.strftime('%m-%d %H:%M')
# dfao["Timestamp"].value_counts()
# values = dfao["Timestamp"].value_counts().keys().tolist()
# counts = dfao["Timestamp"].value_counts().tolist()
# p=pd.DataFrame([counts,values])
# p.T
# p.T.describe()
# p.T.iloc[:,0].sum()
#**
#dfasNormUniq.groupby(['Labels']).corr()
# l1=[0,1,2,3]*2
# x1=[0,1,2,3,4,5,6]
# li=np.repeat(x1,3)
#**
# from cydets.algorithm import detect_cycles
# series = pd.Series(dfas0['Timestamp'].tolist())
# detect_cycles(series)
dfp.head()
| time | label | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 05-01 00:00 | 0 | 0.376665 | -4.596435 | -4.095756 | 13.497687 | -0.118830 | -20.669883 | 0.000732 | -0.061114 | -0.059966 | -0.038189 | 0.877951 | -0.052959 | -13.306135 | 0.101068 | 0.041800 | 0.199901 | -2.327329 | -0.944167 | 3.075199 | 0.123154 | -0.104334 | -0.570710 | -9.784456 | 0.355960 | 15.842819 | -0.451973 | -0.105282 | 96 | -134.27786 | 0.058726 | -0.021645 | 9.366755 | 0.002151 | -69.187583 | 4.232571 | -0.225267 | -0.196872 | -0.072449 | -0.103732 | -0.720746 | -5.412436 | 76.679042 | -0.632727 | 1351.63286 | -0.657095 | -0.434947 | -108.77597 | 0.084856 | 10.210182 | 11.295155 | 29.984624 | 10.091721 | 0.053279 | -4.936434 | -24.590146 | 18.515436 | 3.473400 | 0.033444 | 0.953219 | 0.006076 | 0 |
| 1 | 05-01 00:02 | 0 | 0.475720 | -4.542502 | -4.018359 | 16.230659 | -0.128733 | -18.758079 | 0.000732 | -0.061114 | -0.059966 | -0.038189 | 0.873273 | -0.014244 | -13.306135 | 0.101108 | 0.041447 | 0.304313 | -2.340627 | -0.939994 | 3.075199 | 0.123154 | -0.104334 | -0.574861 | -9.784456 | 0.360160 | 16.491684 | -0.450451 | -0.092430 | 96 | -134.48019 | 0.058759 | -0.004579 | 9.350215 | 0.002149 | -68.585197 | 4.311490 | -0.225267 | -0.196872 | -0.059103 | -0.083895 | -0.720746 | -8.343222 | 78.181598 | -0.632727 | 1370.37895 | -0.875629 | -1.125819 | -108.84897 | 0.085146 | 12.534340 | 11.290761 | 29.984624 | 10.095871 | 0.062801 | -4.937179 | -32.413266 | 22.760065 | 2.682933 | 0.033536 | 1.090502 | 0.006083 | 0 |
| 2 | 05-01 00:04 | 0 | 0.363848 | -4.681394 | -4.353147 | 14.127998 | -0.138636 | -17.836632 | 0.010803 | -0.061114 | -0.030057 | -0.018352 | 1.004910 | 0.065150 | -9.619596 | 0.101148 | 0.041095 | 0.252839 | -2.353925 | -0.935824 | 3.075199 | 0.123154 | -0.104334 | -0.579013 | -9.784456 | 0.364356 | 15.972885 | -0.448927 | -0.097144 | 96 | -133.94659 | 0.058791 | -0.084658 | 9.037409 | 0.002148 | -67.838187 | 4.809914 | -0.225267 | -0.186801 | -0.048696 | -0.073823 | -0.720746 | -1.085166 | 79.684154 | -0.632727 | 1368.12309 | -0.037775 | -0.519541 | -109.08658 | 0.085436 | 18.582893 | 11.286366 | 29.984624 | 10.100265 | 0.072322 | -4.937924 | -34.183774 | 27.004663 | 3.537487 | 0.033629 | 1.840540 | 0.006090 | 0 |
| 3 | 05-01 00:06 | 0 | 0.301590 | -4.758934 | -4.023612 | 13.161567 | -0.148142 | -18.517601 | 0.002075 | -0.061114 | -0.019986 | -0.008280 | 0.930037 | -0.067199 | -15.196531 | 0.101188 | 0.040742 | 0.072873 | -2.367223 | -0.931651 | 3.075199 | 0.123154 | -0.104334 | -0.583165 | -9.784456 | 0.368556 | 15.608688 | -0.447404 | -0.160073 | 96 | -134.00259 | 0.058824 | -0.055118 | 9.020625 | 0.002146 | -67.091148 | 5.308343 | -0.225267 | -0.186801 | -0.047017 | -0.063752 | -0.720746 | 6.172891 | 81.186702 | -0.632727 | 1365.69145 | -0.987410 | 0.674524 | -109.56277 | 0.085726 | 17.719032 | 11.281972 | 29.984624 | 10.104660 | 0.081600 | -4.938669 | -35.954281 | 21.672449 | 3.986095 | 0.033721 | 2.554880 | 0.006097 | 0 |
| 4 | 05-01 00:08 | 0 | 0.265578 | -4.749928 | -4.333150 | 15.267340 | -0.155314 | -17.505913 | 0.000732 | -0.061114 | -0.030057 | -0.008280 | 0.828410 | -0.018472 | -14.609266 | 0.101229 | 0.040390 | 0.171033 | -2.380521 | -0.927478 | 3.075199 | 0.123154 | -0.104334 | -0.587316 | -9.784456 | 0.372756 | 15.606125 | -0.445879 | -0.131630 | 96 | -133.14571 | 0.058856 | -0.153851 | 9.344233 | 0.002145 | -65.991813 | 5.806771 | -0.225267 | -0.186801 | -0.057088 | -0.063752 | -0.720746 | -3.379599 | 82.689258 | -0.632727 | 1363.25786 | -0.238445 | -0.063044 | -110.03891 | 0.086016 | 16.855202 | 11.277577 | 29.984624 | 10.109054 | 0.091121 | -4.939414 | -37.724789 | 21.907251 | 3.601573 | 0.033777 | 1.410494 | 0.006105 | 0 |
dfp=dfp.reset_index()
dfp.index=dfp["time"]
dfp.drop(['time','index'], axis=1, inplace=True)
dfpNorm=dfp[dfp["label"]==0]
dfpAnorm=dfp[dfp["label"]!=0]
dfp[:5]
| label | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 05-01 00:00 | 0 | 0.376665 | -4.596435 | -4.095756 | 13.497687 | -0.118830 | -20.669883 | 0.000732 | -0.061114 | -0.059966 | -0.038189 | 0.877951 | -0.052959 | -13.306135 | 0.101068 | 0.041800 | 0.199901 | -2.327329 | -0.944167 | 3.075199 | 0.123154 | -0.104334 | -0.570710 | -9.784456 | 0.355960 | 15.842819 | -0.451973 | -0.105282 | 96 | -134.27786 | 0.058726 | -0.021645 | 9.366755 | 0.002151 | -69.187583 | 4.232571 | -0.225267 | -0.196872 | -0.072449 | -0.103732 | -0.720746 | -5.412436 | 76.679042 | -0.632727 | 1351.63286 | -0.657095 | -0.434947 | -108.77597 | 0.084856 | 10.210182 | 11.295155 | 29.984624 | 10.091721 | 0.053279 | -4.936434 | -24.590146 | 18.515436 | 3.473400 | 0.033444 | 0.953219 | 0.006076 | 0 |
| 05-01 00:02 | 0 | 0.475720 | -4.542502 | -4.018359 | 16.230659 | -0.128733 | -18.758079 | 0.000732 | -0.061114 | -0.059966 | -0.038189 | 0.873273 | -0.014244 | -13.306135 | 0.101108 | 0.041447 | 0.304313 | -2.340627 | -0.939994 | 3.075199 | 0.123154 | -0.104334 | -0.574861 | -9.784456 | 0.360160 | 16.491684 | -0.450451 | -0.092430 | 96 | -134.48019 | 0.058759 | -0.004579 | 9.350215 | 0.002149 | -68.585197 | 4.311490 | -0.225267 | -0.196872 | -0.059103 | -0.083895 | -0.720746 | -8.343222 | 78.181598 | -0.632727 | 1370.37895 | -0.875629 | -1.125819 | -108.84897 | 0.085146 | 12.534340 | 11.290761 | 29.984624 | 10.095871 | 0.062801 | -4.937179 | -32.413266 | 22.760065 | 2.682933 | 0.033536 | 1.090502 | 0.006083 | 0 |
| 05-01 00:04 | 0 | 0.363848 | -4.681394 | -4.353147 | 14.127998 | -0.138636 | -17.836632 | 0.010803 | -0.061114 | -0.030057 | -0.018352 | 1.004910 | 0.065150 | -9.619596 | 0.101148 | 0.041095 | 0.252839 | -2.353925 | -0.935824 | 3.075199 | 0.123154 | -0.104334 | -0.579013 | -9.784456 | 0.364356 | 15.972885 | -0.448927 | -0.097144 | 96 | -133.94659 | 0.058791 | -0.084658 | 9.037409 | 0.002148 | -67.838187 | 4.809914 | -0.225267 | -0.186801 | -0.048696 | -0.073823 | -0.720746 | -1.085166 | 79.684154 | -0.632727 | 1368.12309 | -0.037775 | -0.519541 | -109.08658 | 0.085436 | 18.582893 | 11.286366 | 29.984624 | 10.100265 | 0.072322 | -4.937924 | -34.183774 | 27.004663 | 3.537487 | 0.033629 | 1.840540 | 0.006090 | 0 |
| 05-01 00:06 | 0 | 0.301590 | -4.758934 | -4.023612 | 13.161567 | -0.148142 | -18.517601 | 0.002075 | -0.061114 | -0.019986 | -0.008280 | 0.930037 | -0.067199 | -15.196531 | 0.101188 | 0.040742 | 0.072873 | -2.367223 | -0.931651 | 3.075199 | 0.123154 | -0.104334 | -0.583165 | -9.784456 | 0.368556 | 15.608688 | -0.447404 | -0.160073 | 96 | -134.00259 | 0.058824 | -0.055118 | 9.020625 | 0.002146 | -67.091148 | 5.308343 | -0.225267 | -0.186801 | -0.047017 | -0.063752 | -0.720746 | 6.172891 | 81.186702 | -0.632727 | 1365.69145 | -0.987410 | 0.674524 | -109.56277 | 0.085726 | 17.719032 | 11.281972 | 29.984624 | 10.104660 | 0.081600 | -4.938669 | -35.954281 | 21.672449 | 3.986095 | 0.033721 | 2.554880 | 0.006097 | 0 |
| 05-01 00:08 | 0 | 0.265578 | -4.749928 | -4.333150 | 15.267340 | -0.155314 | -17.505913 | 0.000732 | -0.061114 | -0.030057 | -0.008280 | 0.828410 | -0.018472 | -14.609266 | 0.101229 | 0.040390 | 0.171033 | -2.380521 | -0.927478 | 3.075199 | 0.123154 | -0.104334 | -0.587316 | -9.784456 | 0.372756 | 15.606125 | -0.445879 | -0.131630 | 96 | -133.14571 | 0.058856 | -0.153851 | 9.344233 | 0.002145 | -65.991813 | 5.806771 | -0.225267 | -0.186801 | -0.057088 | -0.063752 | -0.720746 | -3.379599 | 82.689258 | -0.632727 | 1363.25786 | -0.238445 | -0.063044 | -110.03891 | 0.086016 | 16.855202 | 11.277577 | 29.984624 | 10.109054 | 0.091121 | -4.939414 | -37.724789 | 21.907251 | 3.601573 | 0.033777 | 1.410494 | 0.006105 | 0 |
print(dfp.shape)
print(dfpNorm.shape)
print(dfpAnorm.shape)
(18398, 62) (18274, 62) (124, 62)
print("dfp:")
pd.DataFrame(dfp.describe().transpose()).iloc[:,[1,2,3,7]]
dfp:
| mean | std | min | max | |
|---|---|---|---|---|
| label | 0.006740 | 0.081822 | 0.000000 | 1.000000 |
| x1 | 0.011824 | 0.742875 | -3.787279 | 3.054156 |
| x2 | 0.157986 | 4.939762 | -17.316550 | 16.742105 |
| x3 | 0.569300 | 5.937178 | -18.198509 | 15.900116 |
| x4 | -9.958345 | 131.033712 | -322.781610 | 334.694098 |
| x5 | 0.006518 | 0.634054 | -1.623988 | 4.239385 |
| x6 | 2.387533 | 37.104012 | -279.408440 | 96.060768 |
| x7 | 0.001647 | 0.108870 | -0.429273 | 1.705590 |
| x8 | -0.004125 | 0.075460 | -0.451141 | 0.788826 |
| x9 | -0.003056 | 0.156047 | -0.120087 | 4.060033 |
| x10 | -0.002511 | 0.106526 | -0.098310 | 2.921802 |
| x11 | -0.011166 | 3.220442 | -26.191152 | 5.774670 |
| x12 | 0.014964 | 2.381027 | -22.223434 | 3.813738 |
| x13 | 1.418065 | 45.801703 | -164.897670 | 143.876437 |
| x14 | 0.003222 | 0.162565 | -0.028775 | 1.999404 |
| x15 | 0.001107 | 0.039619 | -0.623730 | 1.990212 |
| x16 | -0.075376 | 2.279129 | -19.901141 | 1.791415 |
| x17 | 0.138210 | 2.149115 | -14.081542 | 11.817992 |
| x18 | 0.105010 | 1.947106 | -18.313006 | 11.738202 |
| x19 | 0.463652 | 4.895061 | -156.929680 | 25.068058 |
| x20 | -0.001578 | 0.302746 | -1.520451 | 6.512321 |
| x21 | 0.076673 | 1.317702 | -7.106501 | 6.678016 |
| x22 | -0.036124 | 0.833010 | -3.228480 | 1.771673 |
| x23 | -0.088743 | 5.961967 | -236.783570 | 27.417853 |
| x24 | -0.393265 | 4.734010 | -12.411317 | 109.584594 |
| x25 | 0.635612 | 59.847503 | -579.412730 | 19.767685 |
| x26 | 0.048185 | 0.995501 | -2.333210 | 3.917840 |
| x27 | -0.006214 | 0.450702 | -1.807603 | 1.165952 |
| x28 | 100.141646 | 17.642120 | 51.000000 | 139.000000 |
| x29 | 7.054580 | 130.112284 | -228.302190 | 312.635010 |
| x30 | 0.003785 | 0.088395 | -0.262892 | 0.633217 |
| x31 | -0.000296 | 0.118237 | -0.539117 | 0.492164 |
| x32 | -0.400527 | 62.478214 | -608.372930 | 12.157893 |
| x33 | 0.000090 | 0.001070 | -0.001809 | 0.002293 |
| x34 | 4.525465 | 75.696562 | -508.638880 | 143.236489 |
| x35 | 0.050165 | 1.893939 | -4.764285 | 6.829778 |
| x36 | -0.001182 | 0.268737 | -0.945199 | 6.258630 |
| x37 | -0.021693 | 0.279232 | -1.706928 | 0.892944 |
| x38 | -0.004367 | 0.202733 | -0.147118 | 5.344958 |
| x39 | -0.003903 | 0.178014 | -0.163853 | 2.756158 |
| x40 | -0.036444 | 0.967533 | -5.700757 | 1.069164 |
| x41 | -0.006410 | 4.842681 | -58.183432 | 53.087075 |
| x42 | 0.266766 | 57.751465 | -39.592770 | 238.135654 |
| x43 | 0.070846 | 4.961169 | -0.632727 | 42.484479 |
| x44 | 403.959326 | 2155.005113 | -3768.476500 | 4051.738330 |
| x45 | 0.090221 | 4.023940 | -2.753967 | 38.592859 |
| x46 | 0.032000 | 0.756007 | -2.453742 | 9.591302 |
| x47 | -0.983876 | 68.300305 | -174.148590 | 141.322785 |
| x48 | 0.002474 | 1.099839 | -5.484846 | 4.003038 |
| x49 | 5.051040 | 107.164471 | -450.744260 | 533.410530 |
| x50 | 0.602553 | 6.454156 | -23.448985 | 17.828847 |
| x51 | -3.357339 | 348.256716 | -3652.989000 | 40.152348 |
| x52 | 0.380519 | 6.211598 | -187.943440 | 14.180588 |
| x53 | 0.360246 | 14.174273 | -1817.595500 | 11.148006 |
| x54 | 0.173708 | 3.029516 | -8.210370 | 6.637265 |
| x55 | 2.379154 | 67.940694 | -230.574030 | 287.252017 |
| x56 | 9.234953 | 81.274103 | -269.039500 | 252.147455 |
| x57 | 0.233493 | 2.326838 | -12.640370 | 6.922008 |
| x58 | -0.001861 | 0.048732 | -0.149790 | 0.067249 |
| x59 | -0.061522 | 10.394085 | -100.810500 | 6.985460 |
| x60 | 0.001258 | 0.004721 | -0.012229 | 0.020510 |
| x61 | 0.001033 | 0.032120 | 0.000000 | 1.000000 |
dfpd
| tagid | tag-description | id | |
|---|---|---|---|
| 0 | DateTime | DateTime | time |
| 1 | FISHER.RL | EventReel | y |
| 2 | P4:ASSA.RS | RSashScanAvg | x1 |
| 3 | P4:BLD.C1 | CT#1 BLADE PSI | x2 |
| 4 | P4:BLD.C2 | P4 CT#2 BLADE PSI | x3 |
| 5 | P4:BLGWFL.MV | Bleached GWD Flow | x4 |
| 6 | BRSTRL.MV | ShwerTemp | x5 |
| 7 | BSFTPD.MV | BlndStckFloTPD | x6 |
| 8 | BW2SCD.C1 | C1 BW SPREAD CD | x7 |
| 9 | BW2SCD.RS | RS BW SPREAD CD | x8 |
| 10 | BW2SMD.C1 | C1 BW SPREAD MD | x9 |
| 11 | BW2SMD.RS | RS BW SPREAD MD | x10 |
| 12 | BWSA.C1 | C1 BW SCAN AVG | x11 |
| 13 | P4:BWSA.RS | RS BW SCAN AVG | x12 |
| 14 | P4:CBFLOW.MV | CoatBrkFlo | x13 |
| 15 | P4:CLAY.MV | Clay Flow | x14 |
| 16 | COUCHV.MV | CouchLoVac | x15 |
| 17 | P4:COUVAC | COUCH VAC | x16 |
| 18 | D40161.MV | 4PrsTopLd | x17 |
| 19 | D40162.MV | 4PrsBotLod | x18 |
| 20 | DRW.CA | CalndrDrw | x19 |
| 21 | DRW.D2 | 2DryrDrw | x20 |
| 22 | DRW.D3 | 3DryrDrw | x21 |
| 23 | DRW.D4 | 4DryrDraw | x22 |
| 24 | DRW1.PT | 1PrsTopDrw | x23 |
| 25 | DRW4.PB | 4PrsBotDrw | x24 |
| 26 | FANPMP | FanPmpSpd | x25 |
| 27 | FBHDR.MV | FlBxHdrVac | x26 |
| 28 | FLTBOX.MV | FlatBxVac | x27 |
| 29 | GRDNUM.TX | Grade&Bwt | x28 |
| 30 | GWDFLO.MV | UnblGWDFlo | x29 |
| 31 | HBXPH.MV | Hdbox pH | x30 |
| 32 | HDBXLV.MV | HdBxLiqLvl | x31 |
| 33 | HDTO | TotHead" | x32 |
| 34 | HORIZS.MV | HorzSlcPos | x33 |
| 35 | KRFLOW.MV | KraftFlow | x34 |
| 36 | LOAD.CO | CouchLoad | x35 |
| 37 | MO2SCD.C1 | C1MoSprdCD | x36 |
| 38 | MO2SCD.RS | RSMoSprdCD | x37 |
| 39 | MO2SMD.C1 | C1MoSprdMD | x38 |
| 40 | MO2SMD.RS | RSMoSprdMD | x39 |
| 41 | MOAV.RL | RL MoisAct | x40 |
| 42 | PR1REJ.MV | PrScrRjFlo | x41 |
| 43 | RBFLOW.MV | RwBrkFlo | x42 |
| 44 | RECFLO.MV | RcycFbrFlo | x43 |
| 45 | RETAID.MV | RetnAidFlo | x44 |
| 46 | RSHDRG | RUSH DRAG | x45 |
| 47 | RUSHDG.MV | Rush/Drag | x46 |
| 48 | SILICA.MV | SilicaFlo | x47 |
| 49 | SLCTMP.MV | HBxSlcTemp | x48 |
| 50 | SODALM.MV | SodAlumFlo | x49 |
| 51 | SPD.CO | CouchSpd | x50 |
| 52 | SPD.MA | MachSpd | x51 |
| 53 | SPD1.PT | 1PrsTopSpd | x52 |
| 54 | SPD4.PB | 4PrsBotSpd | x53 |
| 55 | STARCH.MV | WtNStarFlo | x54 |
| 56 | STKFLO.MV | BasWgtFlo | x55 |
| 57 | TMPFLO.MV | TMP Flow | x56 |
| 58 | TOTHD.MV | HBxTotHead | x57 |
| 59 | TRYCON.MV | TrayCons | x58 |
| 60 | UHTMP.RL | UpprHdTmpRL | x59 |
| 61 | VERTSL.MV | VertSlcPos | x60 |
| 62 | EVT.WP | EventPress | x61 |
import xgboost as xg
import imblearn as im
# Draw Plot
def plot_df(df,x,y, title="", xlabel='Value',ylabel='Value', dpi=100):
plt.figure(figsize=(16,10), dpi=dpi)
plt.plot(df,x,y, color='tab:red')
plt.gca().set(title=title, xlabel=xlabel, ylabel=ylabel)
plt.show()
dfp.plot(subplots=True, figsize=(20,40),sharex =False)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B346988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BBE51C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BC19C48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BC52D88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BC8AE88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BCC0F88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD03FC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD38188>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD431C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD7C388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BDE0508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BE18608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BE50688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BE877C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BEBE8C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BEF79C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BF2FB08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BF69B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BF9FC88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BFD8DC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C00FEC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C049FC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C0860C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C0BD1C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C0F52C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C12E408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C166508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C19E5C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C1D7708>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C20E848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C245948>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C27FA48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C2B5B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C2EBBC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C321CC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C35DE08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C394F08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C3D1048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C40A108>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C443208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C479308>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C4B1448>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C4E9548>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C521608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C55A748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C592848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C5CA948>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C603A88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C639B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C671C08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C6A9D08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C6E1E48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C719F48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C758088>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C791188>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C7C8288>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C800388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C8384C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C86F5C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C8A8688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C8E07C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C9188C8>],
dtype=object)
dfpNorm.plot(subplots=True, figsize=(20,40),sharex=True)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B504B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B8B9088>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B562748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B579048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B5918C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B5C6188>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B5FAF88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B632B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B63FAC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B677B48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BAEEA88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BB279C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BB62988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BB99988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690F92948>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690FC9908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691001888>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69103A848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691074808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6910AC7C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6910E57C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69111F788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691158708>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691190708>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6911C76C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691201688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691239688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B88D7C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691292788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6912CB788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691305748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69133E708>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691375688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6913AF688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6913E7648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691420608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691459608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6914925C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6914C9508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691501508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69153C4C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691575488>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6915AA488>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6915E5408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69161E3C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C691656388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69168E388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6916C7348>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6940C02C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6940F92C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694132288>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69416B248>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6941A3248>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6941DB188>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694214148>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69424D108>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694284108>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6942BF0C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6942F5048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694330048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694363FC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69439AF88>],
dtype=object)
dfpAnorm.plot(subplots=True, figsize=(20,40))#,kind='hist',bins=50)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690B1C088>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690C4B988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6908F1B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690906F88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690922888>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690958148>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69098ED88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6909C8248>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6909D4108>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690A06F08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690DEEE08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690E27D48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690E5FD08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690E96CC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690ECFCC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690F08C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690F3FC08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694A07C08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694A43C08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694A7BBC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694AB3B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694AEEB88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694B27A88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694B5FA48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694B97A48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694BD0A08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694C079C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694C40988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694C79948>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694CB2908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694CEB8C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694D248C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694D5D848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694D95808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694DCC808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694E077C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694E40788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694E77708>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694EB16C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694EEB688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694F21648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694F5B648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694F94608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C694FCC588>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C695004588>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C69503C548>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C695075508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6950AE508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6950E8448>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C695121408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6979493C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6979813C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6979BB388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690BE0108>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697A0F8C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697A48888>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697A80848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697ABB848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697AF3808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697B2C748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697B64748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C697B9C708>],
dtype=object)
dfpNorm.plot(kind = 'line', figsize=(20,15))
<matplotlib.axes._subplots.AxesSubplot at 0x2c6984da748>
dfpAnorm.plot(kind = 'line', figsize=(20,15))
<matplotlib.axes._subplots.AxesSubplot at 0x2c69882f048>
pd.plotting.autocorrelation_plot(dfp.iloc[:,2])
<matplotlib.axes._subplots.AxesSubplot at 0x2c6988d5808>
dfp.iloc[:,2].autocorr(lag=1)
0.9699295932047027
# Draw Plot
def plotDfACorr(df):
f1,ax = plt.subplots(len(df.columns),1 , figsize=(18, 50))
for i in range(len(df.columns)):
plot_acf(df.iloc[:,i],ax=ax[i],zero=False,title=df.columns[i])
ax[i].grid()
plt.show()
# Draw Plot
def plotDfPCorr(df):
f1,ax = plt.subplots(len(df.columns),1 , figsize=(18, 50))
for i in range(len(df.columns)):
plot_pacf(df.iloc[:,i],ax=ax[i],zero=False,title=df.columns[i])
ax[i].grid()
plt.show()
plotDfACorr(dfp)
plotDfACorr(dfpNorm)
C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py:546: RuntimeWarning: invalid value encountered in true_divide
plotDfACorr(dfpAnorm)
dfp.columns
Index(['label', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10',
'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20',
'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30',
'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40',
'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50',
'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60',
'x61'],
dtype='object')
plotDfPCorr(dfp.iloc[:,1:])
plotDfPCorr(dfpNorm.iloc[:,1:])
--------------------------------------------------------------------------- LinAlgError Traceback (most recent call last) <ipython-input-31-e803d942de0b> in <module> ----> 1 plotDfPCorr(dfpNorm.iloc[:,1:]) <ipython-input-25-aaf35f1d4eb6> in plotDfPCorr(df) 3 f1,ax = plt.subplots(len(df.columns),1 , figsize=(18, 50)) 4 for i in range(len(df.columns)): ----> 5 plot_pacf(df.iloc[:,i],ax=ax[i],zero=False,title=df.columns[i]) 6 ax[i].grid() 7 plt.show() C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\graphics\tsaplots.py in plot_pacf(x, ax, lags, alpha, method, use_vlines, title, zero, vlines_kwargs, **kwargs) 254 acf_x = pacf(x, nlags=nlags, alpha=alpha, method=method) 255 else: --> 256 acf_x, confint = pacf(x, nlags=nlags, alpha=alpha, method=method) 257 258 _plot_corr(ax, title, acf_x, confint, lags, irregular, use_vlines, C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py in pacf(x, nlags, method, alpha) 799 ret = pacf_ols(x, nlags=nlags, efficient=efficient, unbiased=unbiased) 800 elif method in ('yw', 'ywu', 'ywunbiased', 'yw_unbiased'): --> 801 ret = pacf_yw(x, nlags=nlags, method='unbiased') 802 elif method in ('ywm', 'ywmle', 'yw_mle'): 803 ret = pacf_yw(x, nlags=nlags, method='mle') C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py in pacf_yw(x, nlags, method) 594 pacf = [1.] 595 for k in range(1, nlags + 1): --> 596 pacf.append(yule_walker(x, k, method=method)[0][-1]) 597 return np.array(pacf) 598 C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\regression\linear_model.py in yule_walker(X, order, method, df, inv, demean) 1351 R = toeplitz(r[:-1]) 1352 -> 1353 rho = np.linalg.solve(R, r[1:]) 1354 sigmasq = r[0] - (r[1:]*rho).sum() 1355 if inv: C:\ProgramData\Anaconda3\lib\site-packages\numpy\linalg\linalg.py in solve(a, b) 401 signature = 'DD->D' if isComplexType(t) else 'dd->d' 402 extobj = get_linalg_error_extobj(_raise_linalgerror_singular) --> 403 r = gufunc(a, b, signature=signature, extobj=extobj) 404 405 return wrap(r.astype(result_t, copy=False)) C:\ProgramData\Anaconda3\lib\site-packages\numpy\linalg\linalg.py in _raise_linalgerror_singular(err, flag) 95 96 def _raise_linalgerror_singular(err, flag): ---> 97 raise LinAlgError("Singular matrix") 98 99 def _raise_linalgerror_nonposdef(err, flag): LinAlgError: Singular matrix
plotDfPCorr(dfpAnorm.iloc[:,1:])
def plotDfLegend(df,kind,bins,dfName="",width=18,height=15):
f = plt.figure()
if kind=="hist":
df.plot(kind=kind, ax=f.gca(),figsize=(width, height),bins=bins,grid=True)
else:
df.plot(kind=kind, ax=f.gca(),figsize=(width, height),grid=True)
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#plt.savefig("photos/distributions/"+str(dfName)+"_"+str(df.name)+".jpg", dpi=300, format='jpg')
plt.show()
[plotDfLegend(dfp.iloc[:,i],"hist",50,"dfp") for i in range(len(dfp.columns))]
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
[plotDfLegend(dfpNorm.iloc[:,i],"hist",50,"dfpNorm") for i in range(len(dfpNorm.columns))]
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
[plotDfLegend(dfpAnorm.iloc[:,i],"hist",50,"dfpAnorm") for i in range(len(dfpAnorm.columns))]
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
# l1=[0,1,2,3]*2
# x1=[0,1,2,3,4,5,6]
# li=np.repeat(x1,3)
# # Draw Plot
# def plotDfSnsDist(dfn,dfa,bins=20):
# f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 20))#start row=0&& col=0
# l1=[i for i in range(0,len(dfn.columns))]
# for (n,a, b, cn,ca) in itertools.zip_longest\
# (np.arange(1,len(dfasAnormUniq.columns)),li,[0,1,2]*7,['r','g','b']*7):
# if a== None :
# break
# sns.distplot(df.iloc[:,a],ax=axes[b,bb],color=c,label=df.columns[a],bins=bins)
# sns.despine(left=True)
# plt.setp(axes, xticks=[])
# plt.tight_layout()
# plt.show()
# Draw Plot
def plotDfCompSnsDist(dfn,dfa,bins=20):
f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 20))#start row=0&& col=0
l1=[i for i in range(0,len(dfn.columns))]
for (n,a, b, cn,ca) in itertools.zip_longest\
(np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
l1,['g']*int(len(dfn.columns)+1),['r']*int(len(dfn.columns)+1)):
if n== None :
break
sns.distplot(dfn.iloc[:,n],ax=axes[b,0],color=cn,label=dfn.columns[n],bins=bins)
sns.distplot(dfa.iloc[:,a],ax=axes[b,1],color=ca,label=dfa.columns[a],bins=bins)
sns.despine(left=True)
plt.setp(axes, xticks=[])
plt.tight_layout()
plt.show()
plotDfCompSnsDist(dfpNorm,dfpAnorm)
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:14: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py:487: RuntimeWarning: invalid value encountered in true_divide C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\nonparametric\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars
# l1=[0,1,2,3]*2
# x1=[0,1,2,3,4,5,6]
# li=np.repeat(x1,3)
# # Draw Plot
# def plotDfSnsScatter(df):
# f1,axes = plt.subplots(7,3 , figsize=(20, 20))#start row=0&& col=0
# for (a, b,bb, c) in itertools.zip_longest\
# (np.arange(1,len(df.columns)),li,[0,1,2]*7,['r','g','b']*7):
# if a== None :
# break
# sns.scatterplot(df.index,df.iloc[:,a],ax=axes[b,bb],color=c,label=df.columns[a])
# sns.despine(left=True)
# plt.setp(axes, xticks=[])
# plt.tight_layout()
# plt.show()
def plotDfCompSnsScatter(dfn,dfa):
f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 100))#start row=0&& col=0
l1=[i for i in range(0,len(dfn.columns))]
for (n,a, b, cn,ca) in itertools.zip_longest\
(np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
l1,['g']*int(len(dfn.columns)+1),['r']*int(len(dfn.columns)+1)):
if n== None :
break
sns.scatterplot(dfn.index,dfn.iloc[:,n],ax=axes[b,0],color=cn,label=dfn.columns[n])
sns.scatterplot(dfa.index,dfa.iloc[:,a],ax=axes[b,1],color=ca,label=dfa.columns[a])
sns.despine(left=True)
plt.setp(axes, xticks=[])
plt.tight_layout()
plt.show()
plotDfCompSnsScatter(dfpNorm,dfpAnorm)
# # fig, ax = plt.subplots(figsize=(10,5))
# # ax.matshow(dfn.corr())
# plt.figure(figsize=(40,7))
# plt.matshow(dfas.corr(), fignum=1)
# #plt.matshow(dfn.corr())
# plt.xticks(range(len(dfas.columns)), dfas.columns,rotation=90)
# plt.yticks(range(len(dfas.columns)), dfas.columns)
# plt.colorbar()
# plt.show()
# sns.pairplot(dfasAnorm)
# plt.show()
corr = dfp.corr()
corr.plot(subplots=True, figsize=(20,200),kind='bar',sharex =False,grid=True)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C41D80C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C87D4C48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C881DA48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C8833F48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C8842448>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C885D908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB072248>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB0A8808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB0B2808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB0E99C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB152B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB188B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB1C1C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB1FCDC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB232EC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB26BFC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB2A90C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB2E3208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB31B308>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB355408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB38D548>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB3C5648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB3FE6C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB436808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB46E908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB4A6A08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB4DEB48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB518BC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB551CC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB58AE08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB5C3F08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB601048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB639188>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB673288>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB6AC388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB6E54C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB71C5C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB757688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB78E788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB7C68C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB7FE9C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB838AC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB86FB88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB8A7C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB8E1D88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB919EC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB952FC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB9920C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB9CB208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBA03308>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBA3B408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBA73548>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBAAB648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBAE36C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBB1C808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBB57908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBB8EA08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBBC6B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBBFFC08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBC3AD08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBC71E08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBCA9F48>],
dtype=object)
corr = dfpNorm.corr()
corr.plot(subplots=True, figsize=(20,200),kind='bar',sharex =False,grid=True)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A64BA108>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AD438B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9CA6B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9DC1C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AB6170C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8D541C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8C9D748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8CBC408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8C9E408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8CC95C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8CD2748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A65657C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A65628C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A6559908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8926B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8950388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8929CC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9B9ADC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9B94EC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8748048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A873F148>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C6217208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C61F5348>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BEF90448>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C0160808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C014F688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AD835788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9D07808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8FD38C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8EC6A48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8AD7B48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8B0EC48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A96DA6C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A956FE48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9D2BF48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8BB70C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8504908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8387288>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A70963C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BC3074C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BBF225C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C0531708>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BA1AA788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A84CDC88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A84C79C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A85CBF08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A7334BC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C0B15C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C20EBD88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C056AE88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B84CCF88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8419C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B89A6208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BA0B42C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BC3EE308>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B877C488>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8773608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BA702748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A727F7C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A93468C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A466CA08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A49DCB08>],
dtype=object)
corr = dfpAnorm.corr()
corr.plot(subplots=True, figsize=(20,200),kind='bar',sharex =False,grid=True)
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6713A08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AA3E3D88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B69F4A88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B68EA8C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B683B9C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6A89B08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6AA3048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6AB8C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6ABCCC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6AD4EC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6AFEFC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B170C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B2F1C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B452C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B5D408>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B74548>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B8A648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6BA26C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6BBD808>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6BE1908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6C02A08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6C27B48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6C49BC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6C6BCC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6C8FE08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6CB2F08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6CEA048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6D21148>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6D59248>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6D91348>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6DCA488>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6E03588>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6E3C648>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6E77788>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6EAD888>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6EE8988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6F20A88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6F59B48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6F91C48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6FC9D48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B7002E88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8009F88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8046088>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B80801C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B80B82C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B80F23C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B812B508>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8166608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B819C688>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B81D47C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B820D8C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B82469C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B827EB08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B82B8B88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8330C88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B836AD88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B83A1EC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8459FC8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B84960C8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8511208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8548308>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8582408>],
dtype=object)
# def plotDfCompSnsScatter(dfn,dfa):
# f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 100))#start row=0&& col=0
# l1=[i for i in range(0,len(dfn.columns))]
# for (n,a, b, cn,ca) in itertools.zip_longest\
# (np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
# l1,['g']*int(len(dfn.columns)+1),['r']*int(len(dfn.columns)+1)):
# if n== None :
# break
# sns.scatterplot(dfn.index,dfn.iloc[:,n],ax=axes[b,0],color=cn,label=dfn.columns[n])
# sns.scatterplot(dfa.index,dfa.iloc[:,a],ax=axes[b,1],color=ca,label=dfa.columns[a])
# sns.despine(left=True)
# plt.setp(axes, xticks=[])
# plt.tight_layout()
# plt.show()
dfpNorm.columns
Index(['label', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10',
'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20',
'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30',
'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40',
'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50',
'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60',
'x61'],
dtype='object')
def plotDfCompSnsBar(dfn,dfa):
fig,axes = plt.subplots(len(dfn.columns),2 , figsize=(20, 200))#start row=0&& col=0
l1=[i for i in range(0,len(dfn.columns))]
for (n,a, b,colNormName,colAnormName) in itertools.zip_longest\
(np.arange(0,len(dfn.columns)),np.arange(0,len(dfa.columns)),\
l1,dfn.columns,dfa.columns):
if n== None :
break
dfn.iloc[:,n].plot(kind='bar',ax=axes[b,0],color='g',grid=True)
dfa.iloc[:,a].plot(kind='bar',ax=axes[b,1],color='r',grid=True)
axes[b,0].legend([colNormName])
axes[b,1].legend([colAnormName])
plt.tight_layout()
fig.tight_layout(h_pad=10)
plt.show()
plotDfCompSnsBar(dfpNorm.iloc[:,1:].corr(),dfpAnorm.iloc[:,1:].corr())
# # Draw Plot
# l1=[i for i in range(0,19)]
# def plotDfCompSnsLine(dfn,dfa):
# fig,axes = plt.subplots(18,2 , figsize=(20, 100))#start row=0&& col=0
# for (n,a, b, cn,ca,colNormName,colAnormName) in itertools.zip_longest\
# (np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
# l1,['g']*20,['r']*20,dfn.columns,dfa.columns):
# if n== None :
# break
# # dfn.iloc[:,n].plot(kind='line',ax=axes[b,0],color='g')
# # dfa.iloc[:,a].plot(kind='line',ax=axes[b,1],color='r')
# axes[b,0].plot(dfn.iloc[:,n] ,color='g')
# axes[b,1].plot(dfa.iloc[:,a] ,color='r')
# axes[b,0].legend([colNormName])
# axes[b,1].legend([colAnormName])
# axes[b,0].grid(True)
# axes[b,1].grid(True)
# plt.tight_layout()
# fig.tight_layout(h_pad=10)
# plt.show()
# plotDfCompSnsLine(dfasNormUniq[:500],dfasAnormUniq[:500])
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
# color = 'red' if val < .7 else 'black'
# color2= 'green' if val >= .7 else 'brown'
color =''
if val < .7:
color = 'red'
elif val >= .7:
color ='green'
return 'color: %s' % color
def highlight_max(s):
'''
highlight the maximum in a Series yellow.
'''
is_min = s == s.min()
return ['background-color: yellow' if v else '' for v in is_min]
# s = df.style.applymap(color_negative_red)
# df.style.apply(highlight_max)
# df.style.\
# applymap(color_negative_red).\
# apply(highlight_max)
corr=dfpNorm.corr()
corr.style.applymap(color_negative_red).apply(highlight_max)
| label | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| label | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| x1 | nan | 1 | 0.139318 | -0.0565441 | 0.0602891 | 0.0480106 | -0.120154 | 0.261621 | 0.340732 | -0.0142271 | 0.0160085 | 0.511622 | 0.563777 | 0.143452 | 0.119091 | -0.0378864 | 0.48882 | 0.053944 | 0.0970786 | -0.0307285 | 0.0340567 | 0.107106 | 0.00516743 | -0.0317404 | -0.352303 | 0.494269 | -0.00810471 | 0.0673206 | -0.208359 | -0.272558 | -0.0695299 | 0.00491047 | 0.509797 | -0.186402 | -0.191206 | 0.0216818 | 0.122649 | 0.323769 | 0.0252415 | 0.0649142 | 0.231809 | 0.00926124 | 0.126037 | -0.0373636 | -0.0294873 | -0.477386 | 0.0866356 | 0.19545 | 0.239859 | 0.0690823 | 0.265762 | 0.490246 | 0.280374 | 0.330654 | -0.30387 | 0.0414172 | 0.0913275 | 0.326162 | 0.0145317 | 0.474509 | -0.113276 | nan |
| x2 | nan | 0.139318 | 1 | 0.16831 | -0.139047 | 0.0352364 | -0.0322765 | -0.0562911 | 0.389517 | -0.0152103 | 0.0680678 | 0.364309 | 0.42751 | -0.0779292 | 0.0178728 | 0.0357738 | 0.432256 | 0.171845 | 0.155164 | -0.180453 | -0.0621013 | 0.127789 | 0.186826 | -0.0189994 | -0.0988272 | 0.344558 | 0.0674123 | 0.200011 | -0.0803234 | 0.0373474 | -0.172661 | 0.00191764 | 0.368031 | -0.257282 | -0.0378738 | -0.0821845 | -0.00752687 | 0.378335 | -0.0381105 | 0.084483 | 0.306648 | -0.00237416 | 0.0634885 | 0.0532941 | -0.0398664 | -0.390502 | -0.237085 | 0.229904 | -0.221203 | -0.0976915 | 0.0960661 | 0.341548 | 0.111194 | 0.102683 | 0.213789 | 0.303368 | 0.158201 | 0.00685851 | 0.216456 | 0.345149 | -0.129086 | nan |
| x3 | nan | -0.0565441 | 0.16831 | 1 | -0.275375 | -0.191389 | 0.00815534 | 0.284564 | 0.207489 | 0.0421202 | 0.0482957 | 0.128713 | 0.28909 | -0.163639 | -0.018081 | 0.0691319 | 0.38395 | -0.202347 | -0.264494 | 0.257841 | 0.0349711 | -0.222267 | -0.216045 | 0.0458722 | 0.077973 | 0.336359 | 0.249966 | 0.138206 | 0.450224 | 0.347726 | 0.24174 | 0.0121716 | 0.325596 | 0.212162 | 0.0458771 | 0.156153 | 0.531967 | 0.252916 | 0.0935707 | 0.0648681 | 0.345143 | -0.013194 | -0.0390709 | 0.0397859 | 0.186414 | -0.323115 | 0.0651286 | -0.0781351 | -0.08058 | 0.117814 | -0.0488918 | 0.305934 | -0.0739203 | -0.0545718 | 0.51649 | -0.0170637 | -0.0168746 | -0.0599798 | -0.410667 | 0.334102 | 0.117571 | nan |
| x4 | nan | 0.0602891 | -0.139047 | -0.275375 | 1 | 0.241756 | 0.199256 | -0.038605 | 0.0246039 | 0.0217083 | -0.000190235 | -0.0348156 | -0.0546766 | 0.0639484 | -0.164219 | -0.127363 | -0.0617378 | 0.117143 | 0.174574 | -0.313566 | -0.184474 | 0.108237 | 0.245012 | 0.266608 | -0.016967 | -0.0290543 | -0.330305 | -0.133219 | 0.0536604 | -0.594793 | -0.260467 | 0.000716112 | -0.00306939 | -0.326108 | 0.193999 | -0.379567 | -0.105045 | 0.0329285 | 0.0340205 | -0.0064779 | -0.136037 | 0.00755974 | -0.0493587 | 0.0653526 | -0.509653 | 0.0410113 | 0.212338 | 0.105275 | 0.228061 | 0.0142673 | -0.30843 | -0.0100763 | -0.251466 | 0.0403539 | -0.406099 | -0.209787 | -0.196512 | -0.170823 | -0.111771 | -0.00676215 | -0.295407 | nan |
| x5 | nan | 0.0480106 | 0.0352364 | -0.191389 | 0.241756 | 1 | -0.0282365 | -0.0147787 | 0.300233 | 0.0170035 | 0.0294111 | -0.0088772 | 0.0253902 | 0.014151 | -0.18358 | -0.148825 | 0.0213286 | 0.615422 | 0.659207 | -0.569848 | -0.230296 | 0.308643 | 0.707859 | 0.641743 | -0.349518 | -0.0169264 | -0.0744811 | 0.0486765 | -0.00391371 | -0.0340624 | -0.698912 | -0.00875224 | -0.011132 | -0.547206 | -0.0462034 | -0.261194 | 0.0687608 | 0.418729 | 0.00455699 | 0.00599544 | 0.0976368 | -0.00333453 | 0.0215379 | 0.213784 | -0.384428 | -0.0257264 | -0.0985914 | 0.162634 | 0.132074 | -0.50181 | -0.385475 | 0.00744209 | -0.356368 | 0.32088 | 0.0800751 | 0.069063 | -0.363546 | -0.401554 | 0.0513585 | -0.00144546 | -0.0415871 | nan |
| x6 | nan | -0.120154 | -0.0322765 | 0.00815534 | 0.199256 | -0.0282365 | 1 | -0.123816 | 0.0384009 | -0.0513028 | -0.0601646 | -0.0287412 | 0.0178286 | 0.612096 | 0.0518667 | 0.00499129 | -0.090709 | 0.0607325 | 0.112021 | -0.153649 | 0.019776 | -0.135252 | 0.0217466 | 0.0481684 | -0.0721502 | -0.106145 | -0.032242 | 0.0565584 | -0.0274331 | 0.402161 | -0.0455641 | -0.00702571 | -0.0717225 | -0.118955 | 0.967582 | -0.254721 | 0.0385812 | 0.0655837 | -0.0243436 | -0.0403087 | 0.00684467 | -0.00245723 | -0.332217 | -0.0765862 | -0.1535 | 0.0576385 | -0.0541543 | 0.289869 | -0.0551021 | -0.0203416 | -0.0309407 | -0.0744891 | 0.00349582 | 0.126189 | 0.21518 | 0.241865 | 0.489522 | -0.0143789 | -0.205877 | -0.0856946 | -0.331663 | nan |
| x7 | nan | 0.261621 | -0.0562911 | 0.284564 | -0.038605 | -0.0147787 | -0.123816 | 1 | 0.310503 | 0.212915 | 0.103999 | 0.32745 | 0.369264 | 0.0619645 | 0.00355163 | -0.0804031 | 0.374135 | -0.086021 | -0.16914 | 0.241714 | 0.0670883 | -0.0211727 | -0.124027 | 0.128106 | -0.182091 | 0.401602 | 0.210233 | 0.140878 | 0.0369992 | -0.0243084 | 0.14774 | 0.00029467 | 0.385027 | 0.106149 | -0.164239 | 0.199828 | 0.568135 | 0.241772 | 0.207668 | 0.130811 | 0.248596 | -0.0176266 | 0.0701649 | 0.0154443 | 0.215539 | -0.351448 | 0.192705 | 0.0358031 | 0.182338 | 0.128918 | 0.0418026 | 0.379564 | 0.0296105 | 0.163389 | -0.0589586 | -0.00949318 | -0.116381 | 0.103565 | -0.323578 | 0.379356 | 0.166061 | nan |
| x8 | nan | 0.340732 | 0.389517 | 0.207489 | 0.0246039 | 0.300233 | 0.0384009 | 0.310503 | 1 | 0.0635876 | 0.186033 | 0.512955 | 0.696067 | -0.0237612 | -0.0233556 | -0.0432363 | 0.648422 | 0.360101 | 0.36063 | -0.341053 | -0.199309 | 0.160851 | 0.417736 | 0.321241 | -0.323539 | 0.552306 | 0.165006 | 0.238275 | -0.0162713 | 0.138876 | -0.397796 | 0.00393677 | 0.576146 | -0.373232 | -0.00134439 | -0.0717855 | 0.413883 | 0.890489 | 0.106657 | 0.20524 | 0.460911 | -0.00624058 | 0.118583 | 0.0448143 | -0.234116 | -0.608868 | -0.131869 | 0.359777 | -0.00444582 | -0.303562 | -0.0816714 | 0.569552 | -0.060361 | 0.283283 | 0.277369 | 0.371313 | -0.196452 | -0.113688 | -0.133015 | 0.569729 | -0.136417 | nan |
| x9 | nan | -0.0142271 | -0.0152103 | 0.0421202 | 0.0217083 | 0.0170035 | -0.0513028 | 0.212915 | 0.0635876 | 1 | 0.541919 | 0.0725614 | 0.0560816 | -0.023175 | -0.0287949 | -0.0310752 | 0.0520081 | 0.0161628 | -0.00183446 | 0.00794826 | -0.0357967 | -0.00391499 | 0.023017 | 0.064208 | 0.01431 | 0.070977 | -0.0407594 | -0.0443799 | -0.0141844 | -0.0512176 | -0.0103219 | -0.00691342 | 0.0714848 | -0.0224057 | -0.0591085 | -0.0568086 | 0.0902424 | 0.0265536 | 0.669517 | 0.392962 | 0.0301956 | 0.00474741 | 0.0452137 | -0.0135327 | -0.0326809 | -0.0656973 | 0.0362497 | 0.00411217 | 0.0390268 | -0.0119095 | -0.0645854 | 0.0720553 | -0.0531533 | -0.019489 | -0.0211886 | -0.0263898 | -0.0369128 | -0.0545843 | -0.0828691 | 0.0739987 | -0.014105 | nan |
| x10 | nan | 0.0160085 | 0.0680678 | 0.0482957 | -0.000190235 | 0.0294111 | -0.0601646 | 0.103999 | 0.186033 | 0.541919 | 1 | 0.0671003 | 0.0990185 | -0.0626818 | -0.0224986 | -0.105281 | 0.0887337 | 0.0602285 | 0.0283288 | -0.0164317 | -0.0435741 | 0.00272566 | 0.0473031 | 0.0416213 | -0.0257356 | 0.0858146 | -0.00239491 | 0.0110173 | -0.00752644 | -0.0325068 | -0.0573946 | -0.0102135 | 0.08774 | -0.0438102 | -0.079197 | -0.0318858 | 0.05071 | 0.107745 | 0.363351 | 0.727035 | 0.0488015 | 0.0195294 | 0.109835 | -0.0202145 | -0.0474575 | -0.0961716 | -0.0448347 | 0.0264831 | 0.00382881 | -0.0497488 | -0.0207824 | 0.0862511 | -0.0124593 | -0.0147923 | 0.0143014 | 0.050334 | -0.066781 | -0.0326002 | -0.0241699 | 0.0895997 | -0.0110019 | nan |
| x11 | nan | 0.511622 | 0.364309 | 0.128713 | -0.0348156 | -0.0088772 | -0.0287412 | 0.32745 | 0.512955 | 0.0725614 | 0.0671003 | 1 | 0.825833 | 0.0883123 | 0.0946404 | 0.00466937 | 0.740862 | 0.0272475 | 0.00281223 | 0.0307682 | -0.0364838 | -0.0269112 | 0.00342941 | -0.103056 | -0.192935 | 0.78628 | 0.0658711 | 0.0701991 | -0.21363 | -0.089248 | -0.0144245 | 0.0162132 | 0.797199 | -0.0384611 | -0.0481513 | 0.109281 | 0.131407 | 0.510895 | 0.0700671 | 0.0859702 | 0.462283 | -0.0068911 | 0.00631804 | -0.0160774 | 0.0727835 | -0.791748 | -0.0429665 | 0.127518 | 0.0511298 | 0.0257295 | 0.246045 | 0.785214 | 0.26372 | 0.178923 | -0.10011 | 0.18212 | 0.124696 | 0.231218 | 0.0903505 | 0.781621 | 0.0189735 | nan |
| x12 | nan | 0.563777 | 0.42751 | 0.28909 | -0.0546766 | 0.0253902 | 0.0178286 | 0.369264 | 0.696067 | 0.0560816 | 0.0990185 | 0.825833 | 1 | 0.0238438 | 0.061861 | -0.000592734 | 0.899755 | 0.0800279 | 0.0416241 | 0.00935224 | -0.0438474 | -0.00578689 | 0.0600424 | 0.00927593 | -0.230376 | 0.893914 | 0.152461 | 0.178353 | -0.0664401 | 0.0251223 | -0.0508478 | 0.0116505 | 0.906958 | -0.0943428 | -0.000297165 | 0.0946469 | 0.371485 | 0.698798 | 0.0614946 | 0.10758 | 0.613884 | -0.00830278 | 0.0489038 | 0.00138094 | 0.0591602 | -0.907747 | -0.0426505 | 0.200499 | 0.00436064 | -0.0110192 | 0.173687 | 0.893692 | 0.187282 | 0.22831 | 0.0708808 | 0.271777 | 0.0570937 | 0.156931 | -0.0622133 | 0.889788 | -0.0073835 | nan |
| x13 | nan | 0.143452 | -0.0779292 | -0.163639 | 0.0639484 | 0.014151 | 0.612096 | 0.0619645 | -0.0237612 | -0.023175 | -0.0626818 | 0.0883123 | 0.0238438 | 1 | 0.048796 | -0.0494426 | -0.0824985 | -0.0218088 | -0.0531018 | -0.0308233 | 0.106988 | -0.0967764 | -0.057561 | -0.113487 | -0.162215 | -0.0592611 | -0.0360152 | -0.0930993 | -0.354564 | 0.15399 | 0.019185 | -0.00772105 | -0.0477756 | 0.000675888 | 0.572224 | -0.0155482 | 0.0334912 | 0.00261258 | -0.0145285 | -0.04121 | 0.0191856 | -0.0010565 | -0.613095 | -0.114633 | 0.00912362 | 0.0433391 | -0.0698836 | 0.198406 | 0.0281953 | -0.00442981 | 0.205539 | -0.0641465 | 0.281555 | 0.168528 | -0.105863 | 0.183426 | 0.536755 | 0.199851 | -0.049764 | -0.0643344 | -0.175984 | nan |
| x14 | nan | 0.119091 | 0.0178728 | -0.018081 | -0.164219 | -0.18358 | 0.0518667 | 0.00355163 | -0.0233556 | -0.0287949 | -0.0224986 | 0.0946404 | 0.061861 | 0.048796 | 1 | -0.040846 | 0.0406475 | -0.131423 | -0.114466 | 0.123731 | 0.0823036 | 0.000466678 | -0.195873 | -0.224357 | -0.0422442 | 0.0274276 | 0.0982407 | 0.0451391 | -0.157163 | 0.0939108 | 0.128675 | 0.0036131 | 0.0273435 | 0.0915874 | 0.0411981 | 0.15958 | -0.0489679 | -0.0590131 | -0.038951 | -0.0157355 | -0.0280314 | 0.00658392 | -0.0152595 | -0.0279771 | 0.131478 | -0.0324969 | -0.0539624 | 0.12659 | 0.00658438 | 0.0514347 | 0.252496 | 0.0177358 | 0.254244 | 0.0660793 | -0.0195572 | 0.161863 | 0.190468 | 0.230492 | 0.101677 | 0.0140487 | 0.0263737 | nan |
| x15 | nan | -0.0378864 | 0.0357738 | 0.0691319 | -0.127363 | -0.148825 | 0.00499129 | -0.0804031 | -0.0432363 | -0.0310752 | -0.105281 | 0.00466937 | -0.000592734 | -0.0494426 | -0.040846 | 1 | 0.000236495 | -0.0816599 | -0.0545936 | 0.0534663 | 0.0187438 | -0.0697958 | -0.0776118 | -0.102187 | 0.0875351 | -0.027282 | 0.0787262 | 0.0527372 | 0.0410387 | 0.0436098 | 0.100809 | -0.00453739 | -0.0231831 | 0.000769247 | 0.0225475 | 0.0329571 | -0.0871013 | -0.0742165 | -0.00596822 | -0.0480716 | -0.0752137 | -0.0160054 | -0.0187236 | -0.00588397 | 0.0610149 | 0.0169521 | -0.0198181 | 0.0362535 | -0.0194361 | 0.103519 | 0.0698738 | -0.0297801 | 0.0537214 | -0.0570504 | 0.0817231 | 0.0677484 | 0.152843 | 0.0254657 | 0.0495683 | -0.0294328 | -0.0177554 | nan |
| x16 | nan | 0.48882 | 0.432256 | 0.38395 | -0.0617378 | 0.0213286 | -0.090709 | 0.374135 | 0.648422 | 0.0520081 | 0.0887337 | 0.740862 | 0.899755 | -0.0824985 | 0.0406475 | 0.000236495 | 1 | 0.0527563 | 0.00397405 | 0.0409377 | 0.0101794 | 0.0488139 | 0.0599858 | 0.0618103 | -0.160119 | 0.88898 | 0.270459 | 0.279251 | 0.0381688 | 0.00391601 | -0.035709 | 0.0116215 | 0.893462 | -0.0615946 | -0.095862 | 0.198617 | 0.394552 | 0.679624 | 0.0538859 | 0.0965069 | 0.675629 | -0.0136052 | 0.0559859 | 0.0286878 | 0.0944958 | -0.890797 | 0.000629329 | 0.155762 | -0.0141634 | -0.0219042 | 0.0898071 | 0.830416 | 0.0974914 | 0.165887 | 0.11282 | 0.190383 | -0.0272142 | 0.0742435 | -0.0647787 | 0.882892 | 0.0501863 | nan |
| x17 | nan | 0.053944 | 0.171845 | -0.202347 | 0.117143 | 0.615422 | 0.0607325 | -0.086021 | 0.360101 | 0.0161628 | 0.0602285 | 0.0272475 | 0.0800279 | -0.0218088 | -0.131423 | -0.0816599 | 0.0527563 | 1 | 0.790445 | -0.644698 | -0.181051 | 0.304658 | 0.715067 | 0.576103 | -0.434168 | -0.0381499 | -0.0527963 | 0.0940321 | -0.0282657 | 0.119106 | -0.733208 | -0.00222196 | -0.0143934 | -0.674435 | 0.0281951 | -0.409511 | 0.00796052 | 0.398663 | 0.00409742 | 0.0644896 | 0.106044 | 0.00155615 | 0.133055 | 0.213547 | -0.433347 | -0.0557225 | -0.292132 | 0.326741 | 0.0547037 | -0.508457 | -0.270597 | -0.0124016 | -0.240403 | 0.442653 | 0.195777 | 0.289284 | -0.316031 | -0.346319 | 0.0551867 | -0.0199799 | -0.173293 | nan |
| x18 | nan | 0.0970786 | 0.155164 | -0.264494 | 0.174574 | 0.659207 | 0.112021 | -0.16914 | 0.36063 | -0.00183446 | 0.0283288 | 0.00281223 | 0.0416241 | -0.0531018 | -0.114466 | -0.0545936 | 0.00397405 | 0.790445 | 1 | -0.773058 | -0.254175 | 0.383313 | 0.800909 | 0.620732 | -0.400155 | -0.0988409 | -0.107494 | 0.137133 | -0.0470389 | 0.0967883 | -0.837279 | -0.00466527 | -0.0645837 | -0.760116 | 0.0454616 | -0.479128 | -0.062267 | 0.405262 | -0.0169376 | 0.0142232 | 0.0513651 | 0.00444913 | 0.248409 | 0.21877 | -0.572141 | -0.0028308 | -0.263391 | 0.405459 | 0.0361689 | -0.57953 | -0.37191 | -0.0548533 | -0.335887 | 0.424919 | 0.200687 | 0.28694 | -0.307922 | -0.416905 | 0.115167 | -0.071749 | -0.242229 | nan |
| x19 | nan | -0.0307285 | -0.180453 | 0.257841 | -0.313566 | -0.569848 | -0.153649 | 0.241714 | -0.341053 | 0.00794826 | -0.0164317 | 0.0307682 | 0.00935224 | -0.0308233 | 0.123731 | 0.0534663 | 0.0409377 | -0.644698 | -0.773058 | 1 | 0.417182 | -0.249863 | -0.749419 | -0.510368 | 0.212131 | 0.162364 | 0.258153 | -0.0526798 | 0.196156 | -0.0438193 | 0.86741 | 0.0075969 | 0.105215 | 0.818705 | -0.0996938 | 0.574214 | 0.0646255 | -0.40138 | 0.0132094 | 0.00213775 | -0.0104087 | 0.00264785 | -0.119119 | -0.164193 | 0.85288 | -0.027103 | 0.326893 | -0.394082 | -0.0116412 | 0.701247 | 0.406565 | 0.0965171 | 0.3577 | -0.1749 | -0.188874 | -0.227946 | 0.353196 | 0.486032 | -0.0936264 | 0.105268 | 0.451503 | nan |
| x20 | nan | 0.0340567 | -0.0621013 | 0.0349711 | -0.184474 | -0.230296 | 0.019776 | 0.0670883 | -0.199309 | -0.0357967 | -0.0435741 | -0.0364838 | -0.0438474 | 0.106988 | 0.0823036 | 0.0187438 | 0.0101794 | -0.181051 | -0.254175 | 0.417182 | 1 | 0.0502152 | -0.282367 | -0.161659 | 0.111528 | 0.0552437 | 0.286406 | 0.0652799 | 0.198868 | 0.116216 | 0.345229 | 0.00301402 | 0.0289163 | 0.34452 | 0.0428434 | 0.302332 | -0.0112117 | -0.173614 | -0.0517028 | -0.0650706 | -0.00580851 | 0.0148155 | -0.192535 | -0.0547838 | 0.481986 | 0.00205585 | 0.13169 | -0.172057 | 0.000962406 | 0.235546 | 0.128166 | 0.0226748 | 0.16626 | -0.00251642 | -0.0923854 | -0.189298 | 0.175485 | 0.135409 | -0.00261199 | 0.0239181 | 0.114051 | nan |
| x21 | nan | 0.107106 | 0.127789 | -0.222267 | 0.108237 | 0.308643 | -0.135252 | -0.0211727 | 0.160851 | -0.00391499 | 0.00272566 | -0.0269112 | -0.00578689 | -0.0967764 | 0.000466678 | -0.0697958 | 0.0488139 | 0.304658 | 0.383313 | -0.249863 | 0.0502152 | 1 | 0.404245 | 0.309783 | 0.0227048 | 0.02556 | 0.0402316 | 0.102124 | 0.0256394 | -0.10578 | -0.355084 | 0.0111449 | 0.0308876 | -0.271388 | -0.178167 | -0.0770649 | -0.0224149 | 0.178397 | -0.00645889 | 0.00988869 | -0.0186082 | 0.00362311 | 0.105292 | 0.206862 | -0.116952 | -0.0331336 | 0.0158082 | 0.258767 | -0.217113 | -0.31809 | -0.346052 | 0.0429172 | -0.322762 | -0.00481772 | -0.0632001 | -0.0786333 | -0.199451 | -0.316378 | 0.198628 | 0.0375525 | -0.124167 | nan |
| x22 | nan | 0.00516743 | 0.186826 | -0.216045 | 0.245012 | 0.707859 | 0.0217466 | -0.124027 | 0.417736 | 0.023017 | 0.0473031 | 0.00342941 | 0.0600424 | -0.057561 | -0.195873 | -0.0776118 | 0.0599858 | 0.715067 | 0.800909 | -0.749419 | -0.282367 | 0.404245 | 1 | 0.68043 | -0.399848 | -0.0458583 | -0.0367846 | 0.128458 | 0.000191436 | 0.0261815 | -0.831789 | -0.0079719 | -0.0179835 | -0.755071 | -0.0124967 | -0.378632 | 0.0263851 | 0.504375 | 0.0177981 | 0.0370512 | 0.0833744 | -0.00613009 | 0.105497 | 0.273128 | -0.554523 | -0.042096 | -0.233781 | 0.403584 | 0.0389863 | -0.602044 | -0.384127 | -0.00157977 | -0.361234 | 0.352595 | 0.182505 | 0.219878 | -0.381082 | -0.453145 | 0.0122746 | -0.0190133 | -0.241696 | nan |
| x23 | nan | -0.0317404 | -0.0189994 | 0.0458722 | 0.266608 | 0.641743 | 0.0481684 | 0.128106 | 0.321241 | 0.064208 | 0.0416213 | -0.103056 | 0.00927593 | -0.113487 | -0.224357 | -0.102187 | 0.0618103 | 0.576103 | 0.620732 | -0.510368 | -0.161659 | 0.309783 | 0.68043 | 1 | -0.356544 | -0.0363973 | 0.0952452 | 0.230951 | 0.374347 | 0.106811 | -0.639769 | -0.0060396 | -0.0249162 | -0.635393 | 0.0230634 | -0.398516 | 0.278413 | 0.412477 | 0.0689452 | 0.0263309 | 0.154382 | -0.010816 | 0.114605 | 0.182358 | -0.385985 | 0.00229564 | 0.0421577 | 0.351516 | -0.02498 | -0.401425 | -0.67408 | -0.00115198 | -0.655244 | 0.373814 | 0.309226 | 0.0723982 | -0.468571 | -0.66235 | -0.412069 | -0.0226975 | -0.146558 | nan |
| x24 | nan | -0.352303 | -0.0988272 | 0.077973 | -0.016967 | -0.349518 | -0.0721502 | -0.182091 | -0.323539 | 0.01431 | -0.0257356 | -0.192935 | -0.230376 | -0.162215 | -0.0422442 | 0.0875351 | -0.160119 | -0.434168 | -0.400155 | 0.212131 | 0.111528 | 0.0227048 | -0.399848 | -0.356544 | 1 | -0.0731948 | -0.192129 | -0.231785 | 0.114395 | -0.0536032 | 0.357243 | 0.00568056 | -0.0828802 | 0.48921 | -0.0177672 | 0.140711 | -0.119278 | -0.341113 | -0.00517765 | -0.0590192 | -0.140052 | 0.00226473 | -0.0976875 | -0.198732 | 0.0921399 | 0.0940898 | 0.0943617 | -0.478972 | -0.186901 | 0.107326 | -0.362467 | -0.0568599 | -0.355261 | -0.893503 | -0.0568204 | -0.492168 | 0.0600647 | -0.374304 | 0.349854 | -0.0379781 | 0.0623459 | nan |
| x25 | nan | 0.494269 | 0.344558 | 0.336359 | -0.0290543 | -0.0169264 | -0.106145 | 0.401602 | 0.552306 | 0.070977 | 0.0858146 | 0.78628 | 0.893914 | -0.0592611 | 0.0274276 | -0.027282 | 0.88898 | -0.0381499 | -0.0988409 | 0.162364 | 0.0552437 | 0.02556 | -0.0458583 | -0.0363973 | -0.0731948 | 1 | 0.0706216 | 0.0173213 | 0.00760569 | -0.0754669 | 0.0769252 | 0.0116325 | 0.996402 | 0.0899445 | -0.0948725 | 0.188961 | 0.337089 | 0.561995 | 0.0651683 | 0.085499 | 0.600688 | -0.00299169 | 0.0107298 | 0.0101061 | 0.185361 | -0.975379 | 0.0504993 | -0.0465541 | 0.00666225 | 0.0623013 | 0.113332 | 0.92486 | 0.113628 | 0.0776035 | -0.0709465 | -0.0283642 | 0.00695859 | 0.123814 | -0.0237711 | 0.993994 | 0.134612 | nan |
| x26 | nan | -0.00810471 | 0.0674123 | 0.249966 | -0.330305 | -0.0744811 | -0.032242 | 0.210233 | 0.165006 | -0.0407594 | -0.00239491 | 0.0658711 | 0.152461 | -0.0360152 | 0.0982407 | 0.0787262 | 0.270459 | -0.0527963 | -0.107494 | 0.258153 | 0.286406 | 0.0402316 | -0.0367846 | 0.0952452 | -0.192129 | 0.0706216 | 1 | 0.717271 | 0.247243 | 0.342044 | 0.146685 | 0.00625847 | 0.0471249 | 0.0858547 | -0.0378928 | 0.457727 | 0.33299 | 0.23752 | -0.0110601 | 0.0343144 | 0.151011 | -0.0226014 | -0.0398387 | -0.0486909 | 0.393956 | -0.0497643 | 0.0946078 | 0.301375 | -0.0201849 | 0.0575173 | 0.151903 | 0.0341467 | 0.124595 | 0.234987 | 0.409776 | 0.427044 | 0.0305721 | 0.106809 | -0.193806 | 0.0330238 | 0.191319 | nan |
| x27 | nan | 0.0673206 | 0.200011 | 0.138206 | -0.133219 | 0.0486765 | 0.0565584 | 0.140878 | 0.238275 | -0.0443799 | 0.0110173 | 0.0701991 | 0.178353 | -0.0930993 | 0.0451391 | 0.0527372 | 0.279251 | 0.0940321 | 0.137133 | -0.0526798 | 0.0652799 | 0.102124 | 0.128458 | 0.230951 | -0.231785 | 0.0173213 | 0.717271 | 1 | 0.174461 | 0.221548 | -0.0883318 | 0.00795425 | 0.0243143 | -0.24523 | 0.00634677 | 0.173652 | 0.30613 | 0.305014 | -0.0176503 | 0.0280861 | 0.179665 | -0.0188243 | 0.154568 | -0.00791379 | 0.0128054 | -0.02602 | 0.0891329 | 0.494327 | 0.0245032 | -0.0254679 | -0.0359892 | 0.0058176 | -0.0440156 | 0.238239 | 0.46896 | 0.523203 | -0.0080928 | -0.0131524 | -0.161197 | -0.00519205 | -0.116754 | nan |
| x28 | nan | -0.208359 | -0.0803234 | 0.450224 | 0.0536604 | -0.00391371 | -0.0274331 | 0.0369992 | -0.0162713 | -0.0141844 | -0.00752644 | -0.21363 | -0.0664401 | -0.354564 | -0.157163 | 0.0410387 | 0.0381688 | -0.0282657 | -0.0470389 | 0.196156 | 0.198868 | 0.0256394 | 0.000191436 | 0.374347 | 0.114395 | 0.00760569 | 0.247243 | 0.174461 | 1 | 0.167803 | 0.0763642 | 0.00288335 | -0.0147921 | 0.105287 | 0.0305468 | 0.044616 | 0.237035 | 0.0379534 | 0.0291802 | 0.00251047 | 0.0598296 | -0.00543596 | 0.0349958 | 0.14145 | 0.166794 | 0.0182946 | 0.142808 | -0.143123 | -0.151896 | 0.146007 | -0.387956 | 0.00553467 | -0.415525 | -0.0486361 | 0.369549 | -0.159229 | -0.288521 | -0.36971 | -0.38428 | 0.00625547 | 0.193939 | nan |
| x29 | nan | -0.272558 | 0.0373474 | 0.347726 | -0.594793 | -0.0340624 | 0.402161 | -0.0243084 | 0.138876 | -0.0512176 | -0.0325068 | -0.089248 | 0.0251223 | 0.15399 | 0.0939108 | 0.0436098 | 0.00391601 | 0.119106 | 0.0967883 | -0.0438193 | 0.116216 | -0.10578 | 0.0261815 | 0.106811 | -0.0536032 | -0.0754669 | 0.342044 | 0.221548 | 0.167803 | 1 | -0.0261613 | -0.00642068 | -0.0724457 | 0.0626475 | 0.415424 | 0.0809636 | 0.268479 | 0.216968 | -0.020399 | -0.00927951 | 0.16233 | -0.0101583 | -0.157621 | -0.0111922 | 0.176997 | 0.00693558 | -0.247343 | 0.0842579 | -0.256311 | -0.271464 | -0.0225483 | -0.047287 | -0.0540626 | 0.0541521 | 0.731979 | 0.313053 | 0.0445636 | -0.150614 | -0.179987 | -0.0607778 | 0.034996 | nan |
| x30 | nan | -0.0695299 | -0.172661 | 0.24174 | -0.260467 | -0.698912 | -0.0455641 | 0.14774 | -0.397796 | -0.0103219 | -0.0573946 | -0.0144245 | -0.0508478 | 0.019185 | 0.128675 | 0.100809 | -0.035709 | -0.733208 | -0.837279 | 0.86741 | 0.345229 | -0.355084 | -0.831789 | -0.639769 | 0.357243 | 0.0769252 | 0.146685 | -0.0883318 | 0.0763642 | -0.0261613 | 1 | 0.00931211 | 0.0398144 | 0.814226 | 0.00851976 | 0.46377 | 0.0199321 | -0.47347 | 0.0105331 | -0.0269741 | -0.0790784 | -0.00259825 | -0.152891 | -0.210303 | 0.693801 | 0.0339897 | 0.293512 | -0.398353 | -0.0415317 | 0.681262 | 0.402712 | 0.0275809 | 0.352127 | -0.333512 | -0.180156 | -0.248929 | 0.409501 | 0.481131 | -0.0760282 | 0.0388113 | 0.249354 | nan |
| x31 | nan | 0.00491047 | 0.00191764 | 0.0121716 | 0.000716112 | -0.00875224 | -0.00702571 | 0.00029467 | 0.00393677 | -0.00691342 | -0.0102135 | 0.0162132 | 0.0116505 | -0.00772105 | 0.0036131 | -0.00453739 | 0.0116215 | -0.00222196 | -0.00466527 | 0.0075969 | 0.00301402 | 0.0111449 | -0.0079719 | -0.0060396 | 0.00568056 | 0.0116325 | 0.00625847 | 0.00795425 | 0.00288335 | -0.00642068 | 0.00931211 | 1 | 0.0115195 | 0.0033291 | -0.00776327 | 0.00454099 | 0.00301158 | 0.0012726 | -0.00260209 | -0.0143602 | 0.0187924 | -0.00525865 | 0.00577653 | -0.00733157 | 0.00189801 | -0.0100602 | 0.148886 | 0.00347012 | 0.00718216 | 0.0100772 | 0.00120113 | 0.0105343 | 0.00304011 | -0.00627562 | -0.00140943 | 0.00193856 | 0.000575786 | 0.0472539 | -0.000105005 | 0.0112286 | 0.00239289 | nan |
| x32 | nan | 0.509797 | 0.368031 | 0.325596 | -0.00306939 | -0.011132 | -0.0717225 | 0.385027 | 0.576146 | 0.0714848 | 0.08774 | 0.797199 | 0.906958 | -0.0477756 | 0.0273435 | -0.0231831 | 0.893462 | -0.0143934 | -0.0645837 | 0.105215 | 0.0289163 | 0.0308876 | -0.0179835 | -0.0249162 | -0.0828802 | 0.996402 | 0.0471249 | 0.0243143 | -0.0147921 | -0.0724457 | 0.0398144 | 0.0115195 | 1 | 0.0340647 | -0.067267 | 0.133935 | 0.332299 | 0.582052 | 0.0667259 | 0.0897804 | 0.599425 | -0.00376172 | 0.0232881 | 0.0130034 | 0.122678 | -0.981353 | 0.0362379 | 0.000834765 | 0.0102709 | 0.0387981 | 0.101548 | 0.928526 | 0.107814 | 0.0877646 | -0.0600288 | -0.00385419 | 0.00947095 | 0.112394 | -0.0275165 | 0.994657 | 0.0685701 | nan |
| x33 | nan | -0.186402 | -0.257282 | 0.212162 | -0.326108 | -0.547206 | -0.118955 | 0.106149 | -0.373232 | -0.0224057 | -0.0438102 | -0.0384611 | -0.0943428 | 0.000675888 | 0.0915874 | 0.000769247 | -0.0615946 | -0.674435 | -0.760116 | 0.818705 | 0.34452 | -0.271388 | -0.755071 | -0.635393 | 0.48921 | 0.0899445 | 0.0858547 | -0.24523 | 0.105287 | 0.0626475 | 0.814226 | 0.0033291 | 0.0340647 | 1 | -0.0559188 | 0.627752 | -0.00565856 | -0.392033 | -0.0171517 | -0.0455273 | -0.0655968 | 0.0079954 | -0.18689 | -0.179689 | 0.738044 | 0.01477 | 0.198509 | -0.675879 | -0.103676 | 0.471824 | 0.309042 | 0.0398549 | 0.246865 | -0.489898 | -0.174769 | -0.383364 | 0.232581 | 0.336406 | 0.10566 | 0.0713894 | 0.503022 | nan |
| x34 | nan | -0.191206 | -0.0378738 | 0.0458771 | 0.193999 | -0.0462034 | 0.967582 | -0.164239 | -0.00134439 | -0.0591085 | -0.079197 | -0.0481513 | -0.000297165 | 0.572224 | 0.0411981 | 0.0225475 | -0.095862 | 0.0281951 | 0.0454616 | -0.0996938 | 0.0428434 | -0.178167 | -0.0124967 | 0.0230634 | -0.0177672 | -0.0948725 | -0.0378928 | 0.00634677 | 0.0305468 | 0.415424 | 0.00851976 | -0.00776327 | -0.067267 | -0.0559188 | 1 | -0.204945 | 0.0092571 | 0.0445773 | -0.0386806 | -0.0645219 | 0.0138491 | -0.0067273 | -0.488993 | -0.0496988 | -0.0802884 | 0.0527481 | -0.0571026 | 0.185537 | -0.103385 | 0.00268653 | -0.0311712 | -0.0687123 | 0.00384585 | 0.0845731 | 0.233525 | 0.177959 | 0.493546 | -0.0279358 | -0.183311 | -0.0764786 | -0.280631 | nan |
| x35 | nan | 0.0216818 | -0.0821845 | 0.156153 | -0.379567 | -0.261194 | -0.254721 | 0.199828 | -0.0717855 | -0.0568086 | -0.0318858 | 0.109281 | 0.0946469 | -0.0155482 | 0.15958 | 0.0329571 | 0.198617 | -0.409511 | -0.479128 | 0.574214 | 0.302332 | -0.0770649 | -0.378632 | -0.398516 | 0.140711 | 0.188961 | 0.457727 | 0.173652 | 0.044616 | 0.0809636 | 0.46377 | 0.00454099 | 0.133935 | 0.627752 | -0.204945 | 1 | 0.115344 | -0.02322 | -0.0553477 | -0.0274856 | 0.120164 | 0.00627464 | -0.181167 | -0.0268831 | 0.667937 | -0.115607 | 0.0722172 | -0.338958 | -0.0425861 | 0.171984 | 0.35732 | 0.129168 | 0.316773 | -0.136152 | -0.0129502 | -0.0771046 | 0.149256 | 0.324531 | 0.137155 | 0.150679 | 0.526969 | nan |
| x36 | nan | 0.122649 | -0.00752687 | 0.531967 | -0.105045 | 0.0687608 | 0.0385812 | 0.568135 | 0.413883 | 0.0902424 | 0.05071 | 0.131407 | 0.371485 | 0.0334912 | -0.0489679 | -0.0871013 | 0.394552 | 0.00796052 | -0.062267 | 0.0646255 | -0.0112117 | -0.0224149 | 0.0263851 | 0.278413 | -0.119278 | 0.337089 | 0.33299 | 0.30613 | 0.237035 | 0.268479 | 0.0199321 | 0.00301158 | 0.332299 | -0.00565856 | 0.0092571 | 0.115344 | 1 | 0.469431 | 0.367323 | 0.105254 | 0.391592 | -0.0202513 | 0.0265289 | 0.0277076 | 0.0505271 | -0.334245 | 0.0990867 | 0.167941 | 0.0414242 | -0.0432544 | -0.151478 | 0.336595 | -0.15949 | 0.132246 | 0.3904 | 0.151506 | -0.177335 | -0.126824 | -0.483007 | 0.332805 | -2.33295e-05 | nan |
| x37 | nan | 0.323769 | 0.378335 | 0.252916 | 0.0329285 | 0.418729 | 0.0655837 | 0.241772 | 0.890489 | 0.0265536 | 0.107745 | 0.510895 | 0.698798 | 0.00261258 | -0.0590131 | -0.0742165 | 0.679624 | 0.398663 | 0.405262 | -0.40138 | -0.173614 | 0.178397 | 0.504375 | 0.412477 | -0.341113 | 0.561995 | 0.23752 | 0.305014 | 0.0379534 | 0.216968 | -0.47347 | 0.0012726 | 0.582052 | -0.392033 | 0.0445773 | -0.02322 | 0.469431 | 1 | 0.0704656 | 0.145113 | 0.526556 | -0.00753903 | 0.0117112 | 0.0995592 | -0.24502 | -0.619835 | -0.132504 | 0.339075 | -0.0129532 | -0.412532 | -0.144673 | 0.578208 | -0.113922 | 0.305727 | 0.372878 | 0.350831 | -0.256273 | -0.188745 | -0.151499 | 0.582964 | -0.132319 | nan |
| x38 | nan | 0.0252415 | -0.0381105 | 0.0935707 | 0.0340205 | 0.00455699 | -0.0243436 | 0.207668 | 0.106657 | 0.669517 | 0.363351 | 0.0700671 | 0.0614946 | -0.0145285 | -0.038951 | -0.00596822 | 0.0538859 | 0.00409742 | -0.0169376 | 0.0132094 | -0.0517028 | -0.00645889 | 0.0177981 | 0.0689452 | -0.00517765 | 0.0651683 | -0.0110601 | -0.0176503 | 0.0291802 | -0.020399 | 0.0105331 | -0.00260209 | 0.0667259 | -0.0171517 | -0.0386806 | -0.0553477 | 0.367323 | 0.0704656 | 1 | 0.464325 | 0.0421244 | -0.00603011 | 0.054874 | -0.0053278 | -0.0386337 | -0.0584077 | 0.0748269 | 0.0307949 | 0.0527379 | 0.0140082 | -0.0646663 | 0.0674794 | -0.0607593 | 0.00280569 | 0.00799887 | -0.00876479 | -0.0657775 | -0.0324572 | -0.142427 | 0.067673 | -0.0117932 | nan |
| x39 | nan | 0.0649142 | 0.084483 | 0.0648681 | -0.0064779 | 0.00599544 | -0.0403087 | 0.130811 | 0.20524 | 0.392962 | 0.727035 | 0.0859702 | 0.10758 | -0.04121 | -0.0157355 | -0.0480716 | 0.0965069 | 0.0644896 | 0.0142232 | 0.00213775 | -0.0650706 | 0.00988869 | 0.0370512 | 0.0263309 | -0.0590192 | 0.085499 | 0.0343144 | 0.0280861 | 0.00251047 | -0.00927951 | -0.0269741 | -0.0143602 | 0.0897804 | -0.0455273 | -0.0645219 | -0.0274856 | 0.105254 | 0.145113 | 0.464325 | 1 | 0.0532512 | 0.0060075 | 0.107575 | 0.00578828 | -0.0386999 | -0.0925692 | -0.0131498 | 0.0785117 | 0.0254153 | -0.0155699 | 0.0113014 | 0.0861259 | 0.01939 | 0.00507707 | 0.0264896 | 0.0661617 | -0.0610659 | 0.0174244 | -0.0595723 | 0.086656 | -0.037507 | nan |
| x40 | nan | 0.231809 | 0.306648 | 0.345143 | -0.136037 | 0.0976368 | 0.00684467 | 0.248596 | 0.460911 | 0.0301956 | 0.0488015 | 0.462283 | 0.613884 | 0.0191856 | -0.0280314 | -0.0752137 | 0.675629 | 0.106044 | 0.0513651 | -0.0104087 | -0.00580851 | -0.0186082 | 0.0833744 | 0.154382 | -0.140052 | 0.600688 | 0.151011 | 0.179665 | 0.0598296 | 0.16233 | -0.0790784 | 0.0187924 | 0.599425 | -0.0655968 | 0.0138491 | 0.120164 | 0.391592 | 0.526556 | 0.0421244 | 0.0532512 | 1 | -0.00654764 | -0.0332595 | 0.0418627 | 0.0873778 | -0.614298 | -0.0736941 | 0.0852757 | -0.0992244 | -0.109646 | -0.0214663 | 0.560958 | 0.00156525 | 0.14746 | 0.269914 | 0.145332 | 0.00263295 | -0.0591472 | -0.154469 | 0.595589 | 0.0295872 | nan |
| x41 | nan | 0.00926124 | -0.00237416 | -0.013194 | 0.00755974 | -0.00333453 | -0.00245723 | -0.0176266 | -0.00624058 | 0.00474741 | 0.0195294 | -0.0068911 | -0.00830278 | -0.0010565 | 0.00658392 | -0.0160054 | -0.0136052 | 0.00155615 | 0.00444913 | 0.00264785 | 0.0148155 | 0.00362311 | -0.00613009 | -0.010816 | 0.00226473 | -0.00299169 | -0.0226014 | -0.0188243 | -0.00543596 | -0.0101583 | -0.00259825 | -0.00525865 | -0.00376172 | 0.0079954 | -0.0067273 | 0.00627464 | -0.0202513 | -0.00753903 | -0.00603011 | 0.0060075 | -0.00654764 | 1 | -0.008309 | -0.00129729 | 0.00255205 | 0.00812009 | 0.0220334 | -0.0217201 | -0.00949391 | 0.00606925 | 0.00817292 | -0.000752098 | 0.0113266 | -0.00103846 | -0.0145956 | -0.0146664 | 0.0109579 | 0.0151336 | 0.0156107 | -0.00325422 | 0.0103384 | nan |
| x42 | nan | 0.126037 | 0.0634885 | -0.0390709 | -0.0493587 | 0.0215379 | -0.332217 | 0.0701649 | 0.118583 | 0.0452137 | 0.109835 | 0.00631804 | 0.0489038 | -0.613095 | -0.0152595 | -0.0187236 | 0.0559859 | 0.133055 | 0.248409 | -0.119119 | -0.192535 | 0.105292 | 0.105497 | 0.114605 | -0.0976875 | 0.0107298 | -0.0398387 | 0.154568 | 0.0349958 | -0.157621 | -0.152891 | 0.00577653 | 0.0232881 | -0.18689 | -0.488993 | -0.181167 | 0.0265289 | 0.0117112 | 0.054874 | 0.107575 | -0.0332595 | -0.008309 | 1 | -0.0162507 | -0.206647 | -0.0197244 | 0.0410536 | 0.109874 | 0.140866 | -0.0215448 | -0.0785415 | 0.0287861 | -0.152196 | 0.0585222 | -0.0196772 | 0.075155 | -0.385211 | -0.0316239 | -0.0235324 | 0.0161996 | 0.00863896 | nan |
| x43 | nan | -0.0373636 | 0.0532941 | 0.0397859 | 0.0653526 | 0.213784 | -0.0765862 | 0.0154443 | 0.0448143 | -0.0135327 | -0.0202145 | -0.0160774 | 0.00138094 | -0.114633 | -0.0279771 | -0.00588397 | 0.0286878 | 0.213547 | 0.21877 | -0.164193 | -0.0547838 | 0.206862 | 0.273128 | 0.182358 | -0.198732 | 0.0101061 | -0.0486909 | -0.00791379 | 0.14145 | -0.0111922 | -0.210303 | -0.00733157 | 0.0130034 | -0.179689 | -0.0496988 | -0.0268831 | 0.0277076 | 0.0995592 | -0.0053278 | 0.00578828 | 0.0418627 | -0.00129729 | -0.0162507 | 1 | -0.0629984 | -0.0261105 | -0.0624661 | 0.0514692 | -0.0059714 | -0.222094 | -0.0196818 | 0.0136648 | 0.0165076 | 0.211834 | 0.118209 | -0.0182296 | -0.207166 | -0.0514224 | -0.0197615 | 0.0104768 | 0.00860898 | nan |
| x44 | nan | -0.0294873 | -0.0398664 | 0.186414 | -0.509653 | -0.384428 | -0.1535 | 0.215539 | -0.234116 | -0.0326809 | -0.0474575 | 0.0727835 | 0.0591602 | 0.00912362 | 0.131478 | 0.0610149 | 0.0944958 | -0.433347 | -0.572141 | 0.85288 | 0.481986 | -0.116952 | -0.554523 | -0.385985 | 0.0921399 | 0.185361 | 0.393956 | 0.0128054 | 0.166794 | 0.176997 | 0.693801 | 0.00189801 | 0.122678 | 0.738044 | -0.0802884 | 0.667937 | 0.0505271 | -0.24502 | -0.0386337 | -0.0386999 | 0.0873778 | 0.00255205 | -0.206647 | -0.0629984 | 1 | -0.078308 | 0.158601 | -0.397789 | -0.132329 | 0.460961 | 0.399318 | 0.119879 | 0.349363 | -0.0587731 | -0.0573426 | -0.13203 | 0.34703 | 0.398813 | 0.0523756 | 0.127178 | 0.511004 | nan |
| x45 | nan | -0.477386 | -0.390502 | -0.323115 | 0.0410113 | -0.0257264 | 0.0576385 | -0.351448 | -0.608868 | -0.0656973 | -0.0961716 | -0.791748 | -0.907747 | 0.0433391 | -0.0324969 | 0.0169521 | -0.890797 | -0.0557225 | -0.0028308 | -0.027103 | 0.00205585 | -0.0331336 | -0.042096 | 0.00229564 | 0.0940898 | -0.975379 | -0.0497643 | -0.02602 | 0.0182946 | 0.00693558 | 0.0339897 | -0.0100602 | -0.981353 | 0.01477 | 0.0527481 | -0.115607 | -0.334245 | -0.619835 | -0.0584077 | -0.0925692 | -0.614298 | 0.00812009 | -0.0197244 | -0.0261105 | -0.078308 | 1 | 0.0854743 | -0.0201039 | 0.0339664 | 0.0342984 | -0.0891918 | -0.913387 | -0.0907314 | -0.10076 | -0.00880714 | -0.0541547 | 0.0137309 | -0.0527736 | -0.00709585 | -0.978741 | -0.0751993 | nan |
| x46 | nan | 0.0866356 | -0.237085 | 0.0651286 | 0.212338 | -0.0985914 | -0.0541543 | 0.192705 | -0.131869 | 0.0362497 | -0.0448347 | -0.0429665 | -0.0426505 | -0.0698836 | -0.0539624 | -0.0198181 | 0.000629329 | -0.292132 | -0.263391 | 0.326893 | 0.13169 | 0.0158082 | -0.233781 | 0.0421577 | 0.0943617 | 0.0504993 | 0.0946078 | 0.0891329 | 0.142808 | -0.247343 | 0.293512 | 0.148886 | 0.0362379 | 0.198509 | -0.0571026 | 0.0722172 | 0.0990867 | -0.132504 | 0.0748269 | -0.0131498 | -0.0736941 | 0.0220334 | 0.0410536 | -0.0624661 | 0.158601 | 0.0854743 | 1 | -0.0719917 | 0.220563 | 0.307106 | -0.100635 | 0.0344549 | -0.0837403 | -0.0841844 | -0.224448 | -0.260037 | -0.0176537 | 0.209087 | -0.250248 | 0.0318671 | 0.000862231 | nan |
| x47 | nan | 0.19545 | 0.229904 | -0.0781351 | 0.105275 | 0.162634 | 0.289869 | 0.0358031 | 0.359777 | 0.00411217 | 0.0264831 | 0.127518 | 0.200499 | 0.198406 | 0.12659 | 0.0362535 | 0.155762 | 0.326741 | 0.405459 | -0.394082 | -0.172057 | 0.258767 | 0.403584 | 0.351516 | -0.478972 | -0.0465541 | 0.301375 | 0.494327 | -0.143123 | 0.0842579 | -0.398353 | 0.00347012 | 0.000834765 | -0.675879 | 0.185537 | -0.338958 | 0.167941 | 0.339075 | 0.0307949 | 0.0785117 | 0.0852757 | -0.0217201 | 0.109874 | 0.0514692 | -0.397789 | -0.0201039 | -0.0719917 | 1 | -0.0140347 | -0.169646 | -0.00844647 | -0.0208217 | 0.0601369 | 0.498895 | 0.288607 | 0.712069 | 0.128769 | -0.0121015 | -0.260871 | -0.0535199 | -0.539333 | nan |
| x48 | nan | 0.239859 | -0.221203 | -0.08058 | 0.228061 | 0.132074 | -0.0551021 | 0.182338 | -0.00444582 | 0.0390268 | 0.00382881 | 0.0511298 | 0.00436064 | 0.0281953 | 0.00658438 | -0.0194361 | -0.0141634 | 0.0547037 | 0.0361689 | -0.0116412 | 0.000962406 | -0.217113 | 0.0389863 | -0.02498 | -0.186901 | 0.00666225 | -0.0201849 | 0.0245032 | -0.151896 | -0.256311 | -0.0415317 | 0.00718216 | 0.0102709 | -0.103676 | -0.103385 | -0.0425861 | 0.0414242 | -0.0129532 | 0.0527379 | 0.0254153 | -0.0992244 | -0.00949391 | 0.140866 | -0.0059714 | -0.132329 | 0.0339664 | 0.220563 | -0.0140347 | 1 | 0.134597 | 0.157885 | 0.00157624 | 0.175224 | 0.201223 | -0.2812 | -0.0985797 | -0.119886 | 0.228895 | 0.00251714 | 0.00122947 | -0.0843583 | nan |
| x49 | nan | 0.0690823 | -0.0976915 | 0.117814 | 0.0142673 | -0.50181 | -0.0203416 | 0.128918 | -0.303562 | -0.0119095 | -0.0497488 | 0.0257295 | -0.0110192 | -0.00442981 | 0.0514347 | 0.103519 | -0.0219042 | -0.508457 | -0.57953 | 0.701247 | 0.235546 | -0.31809 | -0.602044 | -0.401425 | 0.107326 | 0.0623013 | 0.0575173 | -0.0254679 | 0.146007 | -0.271464 | 0.681262 | 0.0100772 | 0.0387981 | 0.471824 | 0.00268653 | 0.171984 | -0.0432544 | -0.412532 | 0.0140082 | -0.0155699 | -0.109646 | 0.00606925 | -0.0215448 | -0.222094 | 0.460961 | 0.0342984 | 0.307106 | -0.169646 | 0.134597 | 1 | 0.336194 | 0.0163994 | 0.296784 | -0.0544496 | -0.285701 | -0.115026 | 0.373743 | 0.437426 | -0.0964985 | 0.01826 | 0.137109 | nan |
| x50 | nan | 0.265762 | 0.0960661 | -0.0488918 | -0.30843 | -0.385475 | -0.0309407 | 0.0418026 | -0.0816714 | -0.0645854 | -0.0207824 | 0.246045 | 0.173687 | 0.205539 | 0.252496 | 0.0698738 | 0.0898071 | -0.270597 | -0.37191 | 0.406565 | 0.128166 | -0.346052 | -0.384127 | -0.67408 | -0.362467 | 0.113332 | 0.151903 | -0.0359892 | -0.387956 | -0.0225483 | 0.402712 | 0.00120113 | 0.101548 | 0.309042 | -0.0311712 | 0.35732 | -0.151478 | -0.144673 | -0.0646663 | 0.0113014 | -0.0214663 | 0.00817292 | -0.0785415 | -0.0196818 | 0.399318 | -0.0891918 | -0.100635 | -0.00844647 | 0.157885 | 0.336194 | 1 | 0.0613991 | 0.941301 | 0.310668 | -0.225159 | 0.289285 | 0.413995 | 0.886256 | 0.0931621 | 0.0694328 | 0.156823 | nan |
| x51 | nan | 0.490246 | 0.341548 | 0.305934 | -0.0100763 | 0.00744209 | -0.0744891 | 0.379564 | 0.569552 | 0.0720553 | 0.0862511 | 0.785214 | 0.893692 | -0.0641465 | 0.0177358 | -0.0297801 | 0.830416 | -0.0124016 | -0.0548533 | 0.0965171 | 0.0226748 | 0.0429172 | -0.00157977 | -0.00115198 | -0.0568599 | 0.92486 | 0.0341467 | 0.0058176 | 0.00553467 | -0.047287 | 0.0275809 | 0.0105343 | 0.928526 | 0.0398549 | -0.0687123 | 0.129168 | 0.336595 | 0.578208 | 0.0674794 | 0.0861259 | 0.560958 | -0.000752098 | 0.0287861 | 0.0136648 | 0.119879 | -0.913387 | 0.0344549 | -0.0208217 | 0.00157624 | 0.0163994 | 0.0613991 | 1 | 0.0624338 | 0.0634962 | -0.0428166 | -0.0346453 | -0.0188371 | 0.0685215 | -0.0254397 | 0.924909 | 0.0823038 | nan |
| x52 | nan | 0.280374 | 0.111194 | -0.0739203 | -0.251466 | -0.356368 | 0.00349582 | 0.0296105 | -0.060361 | -0.0531533 | -0.0124593 | 0.26372 | 0.187282 | 0.281555 | 0.254244 | 0.0537214 | 0.0974914 | -0.240403 | -0.335887 | 0.3577 | 0.16626 | -0.322762 | -0.361234 | -0.655244 | -0.355261 | 0.113628 | 0.124595 | -0.0440156 | -0.415525 | -0.0540626 | 0.352127 | 0.00304011 | 0.107814 | 0.246865 | 0.00384585 | 0.316773 | -0.15949 | -0.113922 | -0.0607593 | 0.01939 | 0.00156525 | 0.0113266 | -0.152196 | 0.0165076 | 0.349363 | -0.0907314 | -0.0837403 | 0.0601369 | 0.175224 | 0.296784 | 0.941301 | 0.0624338 | 1 | 0.336813 | -0.237725 | 0.326094 | 0.429789 | 0.867026 | 0.105934 | 0.0711205 | 0.051158 | nan |
| x53 | nan | 0.330654 | 0.102683 | -0.0545718 | 0.0403539 | 0.32088 | 0.126189 | 0.163389 | 0.283283 | -0.019489 | -0.0147923 | 0.178923 | 0.22831 | 0.168528 | 0.0660793 | -0.0570504 | 0.165887 | 0.442653 | 0.424919 | -0.1749 | -0.00251642 | -0.00481772 | 0.352595 | 0.373814 | -0.893503 | 0.0776035 | 0.234987 | 0.238239 | -0.0486361 | 0.0541521 | -0.333512 | -0.00627562 | 0.0877646 | -0.489898 | 0.0845731 | -0.136152 | 0.132246 | 0.305727 | 0.00280569 | 0.00507707 | 0.14746 | -0.00103846 | 0.0585222 | 0.211834 | -0.0587731 | -0.10076 | -0.0841844 | 0.498895 | 0.201223 | -0.0544496 | 0.310668 | 0.0634962 | 0.336813 | 1 | 0.0716525 | 0.476196 | -0.00674448 | 0.290305 | -0.337243 | 0.0376033 | -0.0838036 | nan |
| x54 | nan | -0.30387 | 0.213789 | 0.51649 | -0.406099 | 0.0800751 | 0.21518 | -0.0589586 | 0.277369 | -0.0211886 | 0.0143014 | -0.10011 | 0.0708808 | -0.105863 | -0.0195572 | 0.0817231 | 0.11282 | 0.195777 | 0.200687 | -0.188874 | -0.0923854 | -0.0632001 | 0.182505 | 0.309226 | -0.0568204 | -0.0709465 | 0.409776 | 0.46896 | 0.369549 | 0.731979 | -0.180156 | -0.00140943 | -0.0600288 | -0.174769 | 0.233525 | -0.0129502 | 0.3904 | 0.372878 | 0.00799887 | 0.0264896 | 0.269914 | -0.0145956 | -0.0196772 | 0.118209 | -0.0573426 | -0.00880714 | -0.224448 | 0.288607 | -0.2812 | -0.285701 | -0.225159 | -0.0428166 | -0.237725 | 0.0716525 | 1 | 0.439108 | -0.086108 | -0.337135 | -0.27281 | -0.0578593 | -0.106888 | nan |
| x55 | nan | 0.0414172 | 0.303368 | -0.0170637 | -0.209787 | 0.069063 | 0.241865 | -0.00949318 | 0.371313 | -0.0263898 | 0.050334 | 0.18212 | 0.271777 | 0.183426 | 0.161863 | 0.0677484 | 0.190383 | 0.289284 | 0.28694 | -0.227946 | -0.189298 | -0.0786333 | 0.219878 | 0.0723982 | -0.492168 | -0.0283642 | 0.427044 | 0.523203 | -0.159229 | 0.313053 | -0.248929 | 0.00193856 | -0.00385419 | -0.383364 | 0.177959 | -0.0771046 | 0.151506 | 0.350831 | -0.00876479 | 0.0661617 | 0.145332 | -0.0146664 | 0.075155 | -0.0182296 | -0.13203 | -0.0541547 | -0.260037 | 0.712069 | -0.0985797 | -0.115026 | 0.289285 | -0.0346453 | 0.326094 | 0.476196 | 0.439108 | 1 | 0.165107 | 0.195713 | -0.105076 | -0.0416033 | -0.183884 | nan |
| x56 | nan | 0.0913275 | 0.158201 | -0.0168746 | -0.196512 | -0.363546 | 0.489522 | -0.116381 | -0.196452 | -0.0369128 | -0.066781 | 0.124696 | 0.0570937 | 0.536755 | 0.190468 | 0.152843 | -0.0272142 | -0.316031 | -0.307922 | 0.353196 | 0.175485 | -0.199451 | -0.381082 | -0.468571 | 0.0600647 | 0.00695859 | 0.0305721 | -0.0080928 | -0.288521 | 0.0445636 | 0.409501 | 0.000575786 | 0.00947095 | 0.232581 | 0.493546 | 0.149256 | -0.177335 | -0.256273 | -0.0657775 | -0.0610659 | 0.00263295 | 0.0109579 | -0.385211 | -0.207166 | 0.34703 | 0.0137309 | -0.0176537 | 0.128769 | -0.119886 | 0.373743 | 0.413995 | -0.0188371 | 0.429789 | -0.00674448 | -0.086108 | 0.165107 | 1 | 0.405734 | 0.14967 | -0.0241082 | -0.105891 | nan |
| x57 | nan | 0.326162 | 0.00685851 | -0.0599798 | -0.170823 | -0.401554 | -0.0143789 | 0.103565 | -0.113688 | -0.0545843 | -0.0326002 | 0.231218 | 0.156931 | 0.199851 | 0.230492 | 0.0254657 | 0.0742435 | -0.346319 | -0.416905 | 0.486032 | 0.135409 | -0.316378 | -0.453145 | -0.66235 | -0.374304 | 0.123814 | 0.106809 | -0.0131524 | -0.36971 | -0.150614 | 0.481131 | 0.0472539 | 0.112394 | 0.336406 | -0.0279358 | 0.324531 | -0.126824 | -0.188745 | -0.0324572 | 0.0174244 | -0.0591472 | 0.0151336 | -0.0316239 | -0.0514224 | 0.398813 | -0.0527736 | 0.209087 | -0.0121015 | 0.228895 | 0.437426 | 0.886256 | 0.0685215 | 0.867026 | 0.290305 | -0.337135 | 0.195713 | 0.405734 | 1 | 0.0143647 | 0.0745477 | 0.108493 | nan |
| x58 | nan | 0.0145317 | 0.216456 | -0.410667 | -0.111771 | 0.0513585 | -0.205877 | -0.323578 | -0.133015 | -0.0828691 | -0.0241699 | 0.0903505 | -0.0622133 | -0.049764 | 0.101677 | 0.0495683 | -0.0647787 | 0.0551867 | 0.115167 | -0.0936264 | -0.00261199 | 0.198628 | 0.0122746 | -0.412069 | 0.349854 | -0.0237711 | -0.193806 | -0.161197 | -0.38428 | -0.179987 | -0.0760282 | -0.000105005 | -0.0275165 | 0.10566 | -0.183311 | 0.137155 | -0.483007 | -0.151499 | -0.142427 | -0.0595723 | -0.154469 | 0.0156107 | -0.0235324 | -0.0197615 | 0.0523756 | -0.00709585 | -0.250248 | -0.260871 | 0.00251714 | -0.0964985 | 0.0931621 | -0.0254397 | 0.105934 | -0.337243 | -0.27281 | -0.105076 | 0.14967 | 0.0143647 | 1 | -0.0107415 | 0.108881 | nan |
| x59 | nan | 0.474509 | 0.345149 | 0.334102 | -0.00676215 | -0.00144546 | -0.0856946 | 0.379356 | 0.569729 | 0.0739987 | 0.0895997 | 0.781621 | 0.889788 | -0.0643344 | 0.0140487 | -0.0294328 | 0.882892 | -0.0199799 | -0.071749 | 0.105268 | 0.0239181 | 0.0375525 | -0.0190133 | -0.0226975 | -0.0379781 | 0.993994 | 0.0330238 | -0.00519205 | 0.00625547 | -0.0607778 | 0.0388113 | 0.0112286 | 0.994657 | 0.0713894 | -0.0764786 | 0.150679 | 0.332805 | 0.582964 | 0.067673 | 0.086656 | 0.595589 | -0.00325422 | 0.0161996 | 0.0104768 | 0.127178 | -0.978741 | 0.0318671 | -0.0535199 | 0.00122947 | 0.01826 | 0.0694328 | 0.924909 | 0.0711205 | 0.0376033 | -0.0578593 | -0.0416033 | -0.0241082 | 0.0745477 | -0.0107415 | 1 | 0.101858 | nan |
| x60 | nan | -0.113276 | -0.129086 | 0.117571 | -0.295407 | -0.0415871 | -0.331663 | 0.166061 | -0.136417 | -0.014105 | -0.0110019 | 0.0189735 | -0.0073835 | -0.175984 | 0.0263737 | -0.0177554 | 0.0501863 | -0.173293 | -0.242229 | 0.451503 | 0.114051 | -0.124167 | -0.241696 | -0.146558 | 0.0623459 | 0.134612 | 0.191319 | -0.116754 | 0.193939 | 0.034996 | 0.249354 | 0.00239289 | 0.0685701 | 0.503022 | -0.280631 | 0.526969 | -2.33295e-05 | -0.132319 | -0.0117932 | -0.037507 | 0.0295872 | 0.0103384 | 0.00863896 | 0.00860898 | 0.511004 | -0.0751993 | 0.000862231 | -0.539333 | -0.0843583 | 0.137109 | 0.156823 | 0.0823038 | 0.051158 | -0.0838036 | -0.106888 | -0.183884 | -0.105891 | 0.108493 | 0.108881 | 0.101858 | 1 | nan |
| x61 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
corr=dfpAnorm.corr()
corr.style.applymap(color_negative_red).apply(highlight_max)
# fig, ax = plt.subplots(figsize=(20,7))
# mask = np.zeros_like(dfasAnorm.corr())
# mask[np.triu_indices_from(mask)] = 1
# sns.heatmap(dfasAnorm.corr(), mask= mask, ax= ax, annot= True)
# import plotly.offline as py
# import plotly.graph_objs as go
# plotly.offline.init_notebook_mode()
# py.iplot([{
# 'x': dfasNormUniq.index[:100],
# 'y': dfasNormUniq[col][:100],
# 'name': col
# } for col in dfasNormUniq.columns])
# import plotly.offline as py
# import plotly.graph_objs as go
# plotly.offline.init_notebook_mode()
# py.iplot([{
# 'x': dfasAnorm.index[:100],
# 'y': dfasAnorm[col][:100],
# 'name': col
# } for col in dfasAnorm.columns])
# import plotly.graph_objects as go
# #[:100]
# #.index
# #['Timestamp']
# for i,j in zip(dfas0NormCycle1.columns,dfas0AnormCycle1.columns):
# fig = go.Figure()
# fig.add_trace(go.Scatter(x=dfas0NormCycle1.index , y=dfas0NormCycle1[i] ,name='dfasNorm',mode='lines'))
# fig.add_trace(go.Scatter(x=dfas0AnormCycle1.index , y=dfas0AnormCycle1[j] ,name='dfasAnorm',mode='lines'))
# fig.update_layout(title=f"{i}")
# fig.show()
# #fig.write_html(f"{i}_linePlot_uniq.html")
# dfEfList=[dfao,dfas]
# # Additive time series:
# # Value = Base Level + Trend + Seasonality + Error
# # Multiplicative Time Series:
# # Value = Base Level x Trend x Seasonality x Error
# from statsmodels.tsa.seasonal import seasonal_decompose
# for f in dfEfList:
# for i in f.columns:
# decomposition = seasonal_decompose(dfao[i][:100], model="additive", freq=30)#model='multiplicative'
# fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,3), constrained_layout=True)
# fig.subplots_adjust(wspace=0.15)
# ax1= plt.subplot(121)
# ax1.plot(decomposition.trend)
# ax1.set_title("Trend--> "+i+"")
# ax2 = plt.subplot(122)
# ax2.plot(decomposition.seasonal)
# ax2.set_title("Seasonality--> "+i+"")
# plt.tight_layout()
# plt.show()
# from statsmodels.tsa.seasonal import seasonal_decompose
# result = seasonal_decompose(dfao.iloc[:1000,1], model="additive", freq=30)
# result.plot()
# plt.show()
# # import matplotlib.dates as mdates
# fig, ax = plt.subplots(figsize=(10,7))
# plt.subplots_adjust(hspace=0.5)
# ax0 = plt.subplot(411)
# plt.plot(result.observed)
# ax0.set_title('obs')
# ax1 = plt.subplot(412)
# plt.plot(result.trend)
# ax1.set_title('trend')
# ax2 = plt.subplot(413)
# plt.plot(result.seasonal)
# ax2.set_title('seasonality')
# ax3 = plt.subplot(414)
# plt.plot(result.resid)
# ax3.set_title('residual')
# fig.autofmt_xdate()
# viridis(n = 8,option = "B")
# colorRampPalette(vir)
# #Putting it together
# corrplot(cor(dfao,method = "color",type = "lower",
# sig.level = 0.01, insig = "blank",addCoef.col = "green",col = vir(200))